Digital Commons Network^™

Barn Owls Exert Top-Down Effects On The Abundance And Behavior Of Rodent Pests, Katherine C. Larson

Cal Poly Humboldt theses and projects

Predators can exert top-down effects on their prey by direct consumption, by imposing risk of predation, or a combination of both. However, empirical evidence for these effects is mixed and inconsistent. Barn owls in agriculture present a system with attributes that should, according to theory, lead to strong top-down effects, but research is limited and the distinction between effects on prey abundance and behavior remains unresolved. Understanding the top-down effect of predators on pests is a cornerstone of integrated pest management and important where barn owls may contribute to the control of rodents on farms. Vegetation can mediate the top-down …

Analyzing Stakeholder Perceptions On Biogas Production Incentives Under California’S Low Carbon Fuel Standard, Emily Read

Cal Poly Humboldt theses and projects

Accounting for more than half of California’s methane emissions, the dairy and livestock sector has become a central focus for the state’s emission reduction efforts. As a leader in climate policy, California’s approach to addressing agricultural methane emissions has the potential to shape the future of livestock agriculture on a national and global scale. The state has prioritized anaerobic digesters as the primary strategy for addressing livestock methane, a decision that has been both widely contested and strongly supported by stakeholders. This thesis investigates stakeholder perceptions on livestock biogas under California’s Low Carbon Fuel Standard (LCFS) using interview and survey …

Neurosymbolic Value-Inspired Ai (Why, What, And How), Amit Sheth, Kaushik Roy

Publications

The rapid progression of Artificial Intelligence (AI) systems, facilitated by the advent of Large Language Models (LLMs), has resulted in their widespread application to provide human assistance across diverse industries. This trend has sparked significant discourse centered around the ever-increasing need for LLM-based AI systems to function among humans as part of human society, sharing human values, especially as these systems are deployed in high-stakes settings (e.g., healthcare, autonomous driving, etc.). Towards this end, neurosymbolic AI systems are attractive due to their potential to enable easy-tounderstand and interpretable interfaces for facilitating valuebased decision-making, by leveraging explicit representations of shared values. …

K-Perm: Personalized Response Generation Using Dynamic Knowledge Retrieval And Persona-Adaptive Queries, Kanak Raj, Kaushik Roy, Vamshi Bonagiri, Priyanshul Govil, Krishnaprasad Thirunarayan, Raxit Goswami, Manas Gaur

Publications

Personalizing conversational agents can enhance the quality of conversations and increase user engagement. However, they often lack external knowledge to tend to a user’s persona appropriately. This is particularly crucial for practical applications like mental health support, nutrition planning, culturally sensitive conversations, or reducing toxic behavior in conversational agents. To enhance the relevance and comprehensiveness of personalized responses, we propose using a two-step approach that involves (1) selectively integrating user personas and (2) contextualizing the response with supplementing information from a background knowledge source. We develop K-PERM (Knowledge-guided PErsonalization with Reward Modulation), a dynamic conversational agent that combines these elements. …

Causal Event Graph-Guided Language-Based Spatiotemporal Question Answering, Kaushik Roy, Alessandro Oltramari, Yuxin Zi, Chathurangi Shyalika, Vignesh Narayanan, Amit Sheth

Publications

Large Language Models have excelled at encoding and leveraging language patterns in large text-based corpora for various tasks, including spatiotemporal event-based question answering (QA). However, due to encoding a text-based projection of the world, they have also been shown to lack a fullbodied understanding of such events, e.g., a sense of intuitive physics, and cause-and-effect relationships among events. In this work, we propose using causal event graphs (CEGs) to enhance language understanding of spatiotemporal events in language models, using a novel approach that also provides proofs for the model’s capture of the CEGs. A CEG consists of events denoted by …

Exploring Alternative Approaches To Language Modeling For Learning From Data And Knowledge, Yuxin Zi, Kaushik Roy, Vignesh Narayanan, Amit Sheth

Publications

Despite their wide applications to language understanding tasks, large language models (LLMs) still face challenges such as hallucinations - the occasional fabrication of information, and alignment issues - the lack of associations with human-curated world models (e.g., intuitive physics or common-sense knowledge). Additionally, the black-box nature of LLMs makes it highly challenging to train them meaningfully in order to achieve a desired behavior. Specifically, the attempt to adjust LLMs’ concept embedding spaces can be highly intractable, which involves analyzing the implicit impact on LLMs’ numerous parameters and the resulting inductive biases. This paper proposes a novel architecture that wraps powerful …

Tutorial: Knowledge-Infused Artificial Intelligence For Mental Healthcare, Kaushik Roy

Publications

Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …

Ai Trustworthy: Ethical Challenges And Strategies, Jian Liu, Iwan Sandjaja, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This paper explores the pivotal role of trust in the widespread application of Artificial Intelligence (AI) across various domains. We review AI applications in sectors like energy, healthcare, and autonomous vehicles and discuss the crisis of human trust they face. This paper introduces a novel framework that delineates the relationship between AI transparency and user trust, highlighting specific industry applications. Through a systematic review of recent literature, we first delve into factors such as emotional response, acceptance, transparency, accuracy, and interpretability that shape human trust in AI. We then underscore the necessity of ethical AI practices and highlight the importance …

Designing High-Performance Identity-Based Quantum Signature Protocol With Strong Security, Sunil Prajapat, Pankaj Kumar, Sandeep Kumar, Ashok Kumar Das, Sachin Shetty, M. Shamim Hossain

VMASC Publications

Due to the rapid advancement of quantum computers, there has been a furious race for quantum technologies in academia and industry. Quantum cryptography is an important tool for achieving security services during quantum communication. Designated verifier signature, a variant of quantum cryptography, is very useful in applications like the Internet of Things (IoT) and auctions. An identity-based quantum-designated verifier signature (QDVS) scheme is suggested in this work. Our protocol features security attributes like eavesdropping, non-repudiation, designated verification, and hiding sources attacks. Additionally, it is protected from attacks on forgery, inter-resending, and impersonation. The proposed scheme benefits from the traditional designated …

Embedding Software Engineering In Mixed Methods: Computationally Enhanced Risk Communication, Ann Marie Reinhold, Madison H. Munro, Elizabeth A. Shanahan, Ross J. Gore, Barry C. Ezell, Clemente I. Izurieta

VMASC Publications

Mixed methods research ameliorates many convergent research challenges within the contemporary sociotechnical landscape. We suggest the integration of software engineering in mixed methods studies is a critical step to address some of the remaining and persistent challenges. One such research challenge where software engineering is particularly well suited is in hazard preparedness—in particular, the creation of risk communication messages to mitigate or prevent harm. Computationally enhanced risk communication is convergent research that integrates software engineering and social science research for the benefit of protecting humans and infrastructure. To this end, we developed a mixed methods framework for the efficient construction …

Cyclegan-Gradient Penalty For Enhancing Android Adversarial Malware Detection In Gray Box Setting, Fabrice Setephin Atedjio, Jean-Pierre Lienou, Frederica F. Nelson, Sachin S. Shetty, Charles A. Kamhoua

VMASC Publications

Adversarial attacks pose significant threats to Android malware detection by undermining the effectiveness of machine learning-based systems. The rapid increase in Android apps complicates the management of malicious software that can compromise user defense solutions. Many current Android defense techniques rely on deep learning methods. Malicious users exploit GAN-based attacks to achieve adversarial attack transferability and deceive target models by crafting adversarial examples based on known models. We propose a new model based on a Cycle Generative Adversarial Network (CycleGAN) to detect GAN-based attacks. This model incorporates a gradient penalty to enhance the detection rate of the target model. Our …

A Defensive Strategy Against Android Adversarial Malware Attacks, Fabrice Setephin Atedjio, Jean-Pierre Lienou, Frederica F. Nelson, Sachin S. Shetty, Charles A. Kamhoua

VMASC Publications

Due to the popularity of Android mobile devices over the past ten years, malicious Android applications have significantly increased. Systems utilizing machine learning techniques have been successfully applied for Android malware detection to counter the constantly changing Android malware threats. However, attackers have developed new strategies to circumvent these systems by using adversarial attacks. An attacker can carefully craft a malicious sample to deceive a classifier. Among the evasion attacks, there is the more potent one, which is based on solid optimization constraints: the Carlini-Wagner attack. Carlini-Wagner is an attack that uses margin loss, which is more efficient than cross-entropy …

Designing A Blockchain-Empowered Telehealth Artifact For Decentralized Identity Management And Trustworthy Communication: Interdisciplinary Approach, Xueping Liang, Nabid Alam, Tahmina Sultana, Eranga Bandara, Sachin Shetty

VMASC Publications

Background: Telehealth played a critical role during the COVID-19 pandemic and continues to function as an essential component of health care. Existing platforms cannot ensure privacy and prevent cyberattacks.

Objective: The main objectives of this study are to understand existing cybersecurity issues in identity management and trustworthy communication processes in telehealth platforms and to design a software architecture integrated with blockchain to improve security and trustworthiness with acceptable performance.

Methods: We improved personal information security in existing telehealth platforms by adopting an innovative interdisciplinary approach combining design science, social science, and computer science in the health care domain, with prototype …

Enhancing Cyber Resilience Through Traffic Generation Patterns In Complex Networks: A Study On Cascading Failures, Aymar Le Père Tchimwa Bouom, Jean-Pierre Lienou, Wilson Ejuh Geh, Frederica Nelson, Sachin Shetty, Charles Kamhoua

VMASC Publications

Network resilience is the capacity of a network to maintain and restore its fundamental operations during or after a failure. This paper investigates the resilience of communication networks with heterogeneous nodes, with host nodes that generate and receive packets and routers that only forward packets. We focus on how traffic generation patterns, defined as the distribution of data packet creation across hosts, affect network resilience. While previous studies identified optimal host placements that balance traffic loads and enhance network performance, this research explores how traffic generation patterns influence network resilience, particularly during cascading failures, where the failure of one node …

Automated Classification Of Elementary Instructional Activities: Analyzing The Consistency Of Human Annotations, Jonathan K. Foster, Peter Youngs, Rachel Van Aswegen, Samarth Singh, Ginger S. Watson, Scott T. Acton

VMASC Publications

Despite a tremendous increase in the use of video for conducting research in classrooms as well as preparing and evaluating teachers, there remain notable challenges to using classroom videos at scale, including time and financial costs. Recent advances in artificial intelligence could make the process of analyzing, scoring, and cataloguing videos more efficient. These advances include natural language processing, automated speech recognition, and deep neural networks. To train artificial intelligence to accurately classify activities in classroom videos, humans must first annotate a set of videos in a consistent way. This paper describes our investigation of the degree of inter-annotator reliability …

Kinetic Simulations Of Dust Grain Charging Near Surfaces Under Various Plasma Conditions, David Lund, Daoru Han

Mechanical and Aerospace Engineering Faculty Research & Creative Works

This paper presents fully kinetic numerical investigations of the charging of spherical and irregular shaped dust grains in multiple configurations utilizing the Parallel Immersed-Finite-Element Particle-in-Cell Dusty (PIFE-PIC-D) framework. The simulations account for both surface charging and in-depth charging of the dust grains immersed in various ambient plasma conditions near a floating or biased surface. PIFE-PIC-D explicitly resolves the geometrical and material properties (permittivity and photoelectron emissions) of each individual dust grain. PIFE-PIC-D will model self-consistently the unsteady and stochastic charging of grains directly from the deposition of electrons and ions onto them. PIFE-PIC-D will also simulate the charging of irregular-shaped …

L∞ Bounds For Transient Growth In Repetitive And Iterative Learning Control Systems, Douglas A. Bristow, John R. Singler

Mechanical and Aerospace Engineering Faculty Research & Creative Works

This paper revisits the problem of large transient growth in Iterative Learning Control (ILC) and Repetitive Process Control (RPC) systems. In ILC and RPC problems a process is repeated iteratively, with new control calculations occurring in between each iteration. Large transient growth refers to the propensity of some control algorithms to grow error exponentially before eventually converging. While robust monotonic convergence algorithms (in which monotonic convergence is guaranteed usually in exchange for a small loss in performance) have largely eliminated the concern for large transient growth in ILC, similar results cannot always be obtained in RPC. The emergence of additive …

Development Of Digital Twin For Fdm Printer With Preventive Cyber-Attack And Control Algorithms, Md Hazrat Ali, Asad Malik, Nursultan Jyeniskhan, Muhammad Arif Mahmood, Essam Shehab, Frank Liou

Mechanical and Aerospace Engineering Faculty Research & Creative Works

This paper presents a developed model of a Digital Twin (DT) for a fused deposition modeling (FDM) printer, real-time defect detection, and proposed frameworks for preventing cyber-attacks in real-time. It also highlights a model predictive control (MPC) algorithm for controlling the material feed based on a real-time feedback system. The system is designed and developed based on DT, and MPC with integrated machine learning (ML) algorithms to establish real-time process control and enhance the safety and reliability of the physical plant. ML algorithm is used for anomaly detection based on the convolutional neural network (CNN) model. The developed system can …

Persistent Net Release Of Carbon Dioxide And Methane From An Alaskan Lowland Boreal Peatland Complex, Eugénie S. Euskirchen, Colin W. Edgar, Evan Kane, Mark P. Waldrop, Rebecca B. Neumann, Kristen L. Manies, Thomas A. Douglas, Catherine Dieleman, Miriam C. Jones, Merritt R. Turetsky

Michigan Tech Publications

Permafrost degradation in peatlands is altering vegetation and soil properties and impacting net carbon storage. We studied four adjacent sites in Alaska with varied permafrost regimes, including a black spruce forest on a peat plateau with permafrost, two collapse scar bogs of different ages formed following thermokarst, and a rich fen without permafrost. Measurements included year-round eddy covariance estimates of net carbon dioxide (CO2), mid-April to October methane (CH4) emissions, and environmental variables. From 2011 to 2022, annual rainfall was above the historical average, snow water equivalent increased, and snow-season duration shortened due to later snow return. Seasonally thawed active …

Valorization Of Spent Vetiver Roots For Biochar Generation, Sameer Neve, Dibyendu Sarkar, Manas Warke, Teresa Bandosz, Rupali Datta

Michigan Tech Publications

Vetiver root is widely used to produce essential oils in the aromatherapy industry. After the extraction of oil, the roots are disposed of as waste. The central objective of this research was to explore the conversion of this waste into a resource using a circular economy framework. To generate biochar, vetiver roots were pyrolyzed at different temperatures (300, 500, and 700 °C) and residence times (30, 60, and 120 min). Analysis showed the root biochar generated at 500 °C and held for 60 min had the highest surface area of 308.15 m2/g and a yield of 53.76%, in addition to …

Resume Content Generation Using Llama 2 With Adapters, Navaneeth Sai Nidadavolu, William B. Andreopoulos

Faculty Research, Scholarly, and Creative Activity

This paper presents a novel approach to enhancing the Llama language model for generating customized resumes tailored to domain-specific job descriptions. Unlike traditional methods that rely heavily on extensive fine-tuning, we implement scalable adapter modules to minimize parameter adjustments. This approach preserves the model's inherent ability to generate coherent and contextually appropriate language across diverse tasks, ensuring that its general linguistic capabilities remain intact. Additionally, we employ a prompting strategy to dynamically create a diverse and comprehensive dataset, ensuring high relevance to various job roles. Using a dataset of 10,000 job descriptions and resumes, our approach resulted in a 20% …

Vehicular Traffic Flow Prediction Via Decentralized Federated Meta-Learning, Andrew Selvia, Ankur Singh, Wencen Wu

Faculty Research, Scholarly, and Creative Activity

Traffic flow prediction (TFP) has become a crucial problem for billions of people who rely on traffic predictions to optimize their travels every day. In this paper, we present a decentralized federated meta-learning (DFML) framework for traffic flow prediction. Within this framework, traffic detectors spread across a road system can form federations with nearby peers in order to predict traffic collaboratively. A node that assembles a federation assumes the role of receiver while the remainder are assigned as broadcasters. The broadcasters learn model parameters from local data and then broadcast those models to receivers, thereby enriching the available information from …

Follow-Up Biomonitoring Study Of Metal Exposure In Apprentice Welders In Montreal, Quebec, During Gas Metal Arc Welding (Gmaw), Jairo Buitrago-Cortes, Philippe Sarazin, Denis Dieme, Jonathan Côté, Capucine Ouellet, Naïma El Majidi, Michèle Bouchard

Études primaires

Welding activities are known to expose workers to metal fumes, but few studies have focused on assessing the internal exposure of apprentices in learning environments. This study aimed at determining internal doses of metals in apprentices performing gas metal arc welding (GMAW) during their training course. A total of 85 apprentice welders were assessed, and multi-elements were measured in urine, hair, fingernail, and toenail samples collected at the beginning of the program, and at the beginning and end of GMAW practical training. Concentrations of welding fumes and metals were also determined in personal respirable air samples. Serial measurements of metal …

Quantifying The Impact Of A Constructed Wetland On Downstream Nitrate Concentrations And Loads In The U.S. Midwest, Elliot Anderson, Keith E. Schilling, Craig L. Just, Bong Chul Seo

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Constructed wetlands are standard conservation practices used to reduce nitrate loads in agricultural watersheds. Many studies have examined the efficiency of denitrification in wetlands under various scenarios, but quantifying the watershed-scale impact of wetlands on downstream nitrate levels is rarely done using field observations. In this study, we estimated nitrate removal in a constructed wetland in the headwaters of Mud Creek, a HUC12 watershed in eastern Iowa, from May–September 2022 and May–September 2023 (a ten-month period). We also measured nitrate loads at four successive downstream sites, three along Mud Creek and one below its confluence with the larger Cedar River. …

Proknow: Process Knowledge For Safety Constrained And Explainable Question Generation For Mental Health Diagnostic Assistance In The Age Of Large Language Models, Kaushik Roy, Manas Gaur, Misagh Soltani, Vipula Rawte, Ashwin Allen, Amit P. Sheth

Publications

Current Virtual Mental Health Assistants (VMHAs) primarily offer counseling and suggestive care but do not assist with patient diagnosis due to their lack of training in safety-constrained and specialized clinical process knowledge, referred to as ProKnow. In this work, we define ProKnow as an ordered set of information aligned with evidence-based guidelines or categories of conceptual understanding used by domain experts. We also introduce a new dataset of diagnostic conversations guided by safety constraints and Pro- Know, known as ProKnow-data. We develop a method for natural language question generation (NLG) designed to interactively gather diagnostic information from patients, termed ProKnow-algo. …

Electrostatic Sorting Of Lunar Regolith Simulants For Sustainable Resource Utilization: Modeling And Characterization Of Particle Size Distribution, Abdullah Al Moinee, Peter Bachle, Kyle Newport, William Schonberg, David Bayless, Jeffrey Smith, Daoru Han, Fateme Rezaei

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

In pursuit of sustainable resource utilization on the Moon, this paper delves into modeling and characterization of particle size distribution (PSD) of lunar regolith simulants in an electrostatic system. A prototype electrostatic sieve was built and tested with four sample simulants mirroring properties of lunar mare and highland regolith. An alternating four-phase (90 degrees, 180 degrees, 270 degrees, 360 degrees) traveling square-wave was utilized for particle-directed transport to model the diverse trajectories of the particles. Numerically, we scrutinized how the distribution functions of the particles are manifested as the electrostatic field propagates, with a focus on three distinct particle ranges …

Listeria Adhesion Protein Orchestrates Caveolae-Mediated Apical Junctional Remodeling Of Epithelial Barrier For Listeria Monocytogenes Translocation, Rishi Drolia, Donald B. Bryant, Shivendra Tenguria, Zuri A. Jules-Culver, Jessie Thind, Breanna Amelunke, Donqi Liu, Nicholas L. F. Gallina, Krishna K. Mishra, Manalee Samaddar, Manoj R. Sawale, Dharmendra K. Mishra, Abigail D. Cox, Arun K. Bhunia

Biological Sciences Faculty Publications

The cellular junctional architecture remodeling by Listeria adhesion protein-heat shock protein 60 (LAP-Hsp60) interaction for Listeria monocytogenes (Lm) passage through the epithelial barrier is incompletely understood. Here, using the gerbil model, permissive to internalin (Inl) A/B-mediated pathways like in humans, we demonstrate that Lm crosses the intestinal villi at 48 h post-infection. In contrast, the single isogenic (lap− or ΔinlA) or double (lap−ΔinlA) mutant strains show significant defects. LAP promotes Lm translocation via endocytosis of cell-cell junctional complex in enterocytes that do not display luminal E-cadherin. In comparison, InlA facilitates …

Automatic Modeling Of Cyber Intrusions Using The Diamond Model Utilizing Security Logs And Events, Mahmoud Al-Maani

Master's Theses and Doctoral Dissertations

Current intrusion analysis models suffer from unreliability and inaccuracy due to their reliance on outdated and inadequate data sources. Numerous models focus on a particular type of data, leading to potential modeling faults in intrusion analysis models' recommendations. The objective of this thesis is to build a modernized model by integrating the diamond model with security information and event management systems. This thesis presents a detailed cyber intrusion analysis model; in which Elasticsearch is being used to collect and analyze logs about cyber attacks and extract major indicators of compromise then finally map them to the diamond model. The results …

Salmonella Detection In Food Using A Hek-Htlr5 Reported Cell-Based Sensor, Esma Eser, Victoria A. Felton, Rishi Drolia, Arun K. Bhunia

Biological Sciences Faculty Publications

The development of a rapid, sensitive, specific method for detecting foodborne pathogens is paramount for supplying safe food to enhance public health safety. Despite the significant improvement in pathogen detection methods, key issues are still associated with rapid methods, such as distinguishing living cells from dead, the pathogenic potential or health risk of the analyte at the time of consumption, the detection limit, and the sample-to-result. Mammalian cell-based assays analyze pathogens’ interaction with host cells and are responsive only to live pathogens or active toxins. In this study, a human embryonic kidney (HEK293) cell line expressing Toll-Like Receptor 5 (TLR-5) …

Receptor-Targeted Next-Generation Probiotics Ameliorate Mammalian Colitis, Nicholas L. F. Gallina, Vignesh Nathan, Akshay Krishnakumar, Dongqi Liu, Rishi Drolia, Nicole Irrizary Tardi, Yang Fu, Manalee Samadar, Shivendra Tenguria, Alvin Cai, Ruth Eunice Centeno-Martinez, Timothy A. Johnson, Abigail Cox, Lavanya Reddivari, Bruce Applegate, Xingjian Bai, Luping Xu, Deepti Tanjore, Ramesh Vemulapalli, Rahim Rahimi, Arun K. Bhunia

Biological Sciences Faculty Publications

Introduction: a loss of intestinal barrier function, inflammation, and an elevated expression of epithelial heat shock protein 60 (Hsp60) are features of an inflamed bowel. Probiotics have been used to alleviate colitis-induced pathologies, but offer poor adhesion and adaptation to the diseased gut. We hypothesize that enhancing probiotic adhesion in the inflamed bowel may ameliorate such pathologies. Listeria adhesion protein (LAP; 94-kDa acetaldehyde alcohol dehydrogenase) aids Listeria attachment to the epithelial cells by interacting with the mammalian receptor Hsp60. Bioengineered Lactobacillus casei probiotics (BLPs) expressing LAP showed strong interaction with epithelial Hsp60, a high immunomodulatory response, and sustained epithelial barrier …