Digital Commons Network^™

Using Machine Learning To Predict State Compliance With International Legal Obligations For Registration Of Space Objects: Comparative Performance Of Logistic Regression And Dense Neural Network Models, Jonathan K. Sawmiller

Student Publications

Approximately 12% of satellites and other objects launched into outer space have not been registered with the United Nations (UN) as required by international law. To predict whether States will register a launched space object and understand what factors influence a registration decision, data from a UN online index of space objects was used to train and select the best machine learning model. After preparation, the dataset had 1938 datapoints with 11 features, with categorical features simplified and converted to binary.

Multiple variations of classical logistic regression models were compared to multiple variations of dense neural network models. The best …

A Study On The Polarity Of Generalized Neutrosophic Ideals In Bck-Algebra, Bavanari Satyanarayana, Shake Baji

Neutrosophic Systems with Applications

In this study, we apply k-polar generalized neutrosophic logic to the ideal of BCK-algebra and consequently introduce the notion of a k-polar generalized neutrosophic ideal in BCK-algebra with an example. We provide conditions for a k-polar generalized neutrosophic set to be a k-polar generalized neutrosophic ideal. We prove that every k-polar generalized neutrosophic ideal is a k-polar generalized neutrosophic subalgebra, but the converse is not true, which can be illustrated with an example. Furthermore, we prove that a k-polar generalized neutrosophic set is a k-polar generalized neutrosophic ideal if and only if its corresponding cut sets are ideals of the …

Unveiling Similarities In The Code Of Life: A Detailed Exploration Of Dna Sequence Matching Algorithm, Mahmoud Y. Shams, Romany M. Farag, Dalia A. Aldawody, Huda E. Khalid, Ahmed K. Essa, Hazem M. El-Bakry, A. A. Salama

Neutrosophic Systems with Applications

Identifying similar DNA sequences is crucial in various biological research endeavors. This paper delves into the intricate workings of a specific algorithm designed for this purpose. We provide a systematic explanation, exploring how the algorithm handles user input, reads stored DNA sequences, utilizes the Word2Vec model for vector representation, and calculates sequence similarity using diverse metrics like Cosine Similarity and Neutrosophic Distance. Additionally, the paper explores the incorporation of neutrosophic values to account for uncertainty in the comparisons. Finally, we discuss the extraction of results, including matched sequences, similarity scores, and accuracy measures. This in-depth exploration provides a clear understanding …

Evaluation Of Renewable Energy Sources For A Sustainable Future: A Multi-Criteria Decision-Making Approach, Mai Mohamed, Asmaa Elsayed

Neutrosophic Systems with Applications

The urgent global challenges of climate change, energy security, and environmental degradation highlight the need for sustainable energy solutions. Renewable energy sources (RES) present a viable pathway towards sustainability by mitigating greenhouse gas emissions, reducing reliance on fossil fuels, and fostering economic resilience. Purpose: The purpose of this paper is to propose an advanced Multi-Criteria Decision-Making (MCDM) approach to evaluate various RES by integrating environmental, economic, technological, social acceptance, and resource availability criteria, to identify the most suitable RES for sustainable energy solutions. Methodology: The study employs a hybrid method combining Type-2 Neutrosophic Numbers (T2NN) with LOPCOW (Logarithmic Percentage Change …

Neutrosophic Approach To Water Quality Assessment: A Case Study Of Gomati River, The Largest River In Tripura, India, Ajoy Kanti Das, Nandini Gupta, Carlos Granados, Rakhal Das, Suman Das

Neutrosophic Systems with Applications

This study addresses the complexity of assessing river water quality, a multifaceted process influenced by numerous water quality parameters (WQPs) characterized by inherent uncertainties and diverse judgment information from decision-makers. These uncertainties and diverse judgment information can be effectively represented and simulated using Neutrosophic sets. In this study, we propose an effective water pollution rating system, the Neutrosophic water quality index (NWQI), to derive a water pollution score (NWQI-score) for rating water pollution levels. We demonstrate the application of our methodology through an assessment of water quality indices for rating pollution in the Gomati River, the largest river in Tripura, …

An Approach To Multi-Attribute Decision-Making Based On Single-Valued Neutrosophic Hesitant Fuzzy Aczel-Alsina Aggregation Operator, Raiha Imran, Kifayat Ullah, Zeeshan Ali, Maria Akram

Neutrosophic Systems with Applications

A single-valued Neutrosophic hesitant fuzzy set (SVNHFS) is a combination of a single-valued neutrosophic set (SVNS) and hesitant fuzzy set (HFS) that has been developed to address insufficient, unreliable, and vague environments in which each element has several possible options determined by the truthiness, indeterminacy and falsity value. By considering this, in this paper, we have proposed the Aczel-Alsina aggregation operator (AAAO) for SVNHFS, which is more flexible t-norm and t-conorm than the other and due to the flexible nature of parameters to solve Multi-Attribute decision making (MADM) problems. Further, the score function, accuracy function, and certainty function of SVNHFS …

The Psychological Impacts Of Algorithmic And Ai-Driven Social Media On Teenagers: A Call To Action, Sunil Arora, Sahil Arora, John Hastings

Research & Publications

This study investigates the meta-issues surrounding social media, which, while theoretically designed to enhance social interactions and improve our social lives by facilitating the sharing of personal experiences and life events, often results in adverse psychological impacts. Our investigation reveals a paradoxical outcome: rather than fostering closer relationships and improving social lives, the algorithms and structures that underlie social media platforms inadvertently contribute to a profound psychological impact on individuals, influencing them in unforeseen ways. This phenomenon is particularly pronounced among teenagers, who are disproportionately affected by curated online personas, peer pressure to present a perfect digital image, and the …

Transforming Information Systems Management: A Reference Model For Digital Engineering Integration, John Bonar, John Hastings

Research & Publications

Digital engineering practices offer significant yet underutilized potential for improving information assurance and system lifecycle management. This paper examines how capabilities like model-based engineering, digital threads, and integrated product lifecycles can address gaps in prevailing frameworks. A reference model demonstrates applying digital engineering techniques to a reference information system, exhibiting enhanced traceability, risk visibility, accuracy, and integration. The model links strategic needs to requirements and architecture while reusing authoritative elements across views. Analysis of the model shows digital engineering closes gaps in compliance, monitoring, change management, and risk assessment. Findings indicate purposeful digital engineering adoption could transform cybersecurity, operations, service …

Confronting The Reproducibility Crisis: A Case Study Of Challenges In Cybersecurity Ai, Richard H. Moulton, Gary A. Mccully, John D. Hastings

Research & Publications

In the rapidly evolving field of cybersecurity, ensuring the reproducibility of AI-driven research is critical to maintaining the reliability and integrity of security systems. This paper addresses the reproducibility crisis within the domain of adversarial robustness—a key area in AI-based cybersecurity that focuses on defending deep neural networks against malicious perturbations. Through a detailed case study, we attempt to validate results from prior work on certified robustness using the VeriGauge toolkit, revealing significant challenges due to software and hardware incompatibilities, version conflicts, and obsolescence. Our findings underscore the urgent need for standardized methodologies, containerization, and comprehensive documentation to ensure the …

Setc: A Vulnerability Telemetry Collection Framework, Ryan Holeman, John Hastings, Varghese Mathew Vaidyan

Research & Publications

As emerging software vulnerabilities continuously threaten enterprises and Internet services, there is a critical need for improved security research capabilities. This paper introduces the Security Exploit Telemetry Collection (SETC) framework - an automated framework to generate reproducible vulnerability exploit data at scale for robust defensive security research. SETC deploys configurable environments to execute and record rich telemetry of vulnerability exploits within isolated containers. Exploits, vulnerable services, monitoring tools, and logging pipelines are defined via modular JSON configurations and deployed on demand. Compared to current manual processes, SETC enables automated, customizable, and repeatable vulnerability testing to produce diverse security telemetry. This …

Addressing The Teacher Shortage: A Data-Driven Exploration Of Computer Science Teacher Capacity In Wisconsin High Schools, Sujeeth Goud Ramagoni

Dissertations (1934 -)

The rapid evolution of technology and its integration across virtually every field has made computer science (CS) education a crucial component of modern curricula. Despite the increasing demand for CS skills and being the top source of new wage creation in the U.S., CS education remains undervalued and underrepresented in high school systems. This disparity is particularly evident in Wisconsin (WI), where the lack of certified CS teachers creates significant barriers to student access and equitable participation in CS courses. Although WI implemented a CS teacher certification requirement over 30 years ago and was among the earlier states to adopt …

Weed Seed Wizard Case Study - Don't Stop Harvest Weed Management Because It’S A Dry Year, Department Of Primary Industries And Regional Development, Western Australia

Biosecurity research reports

The Weed Seed Wizard is a national collaborative project that uses paddock management information to predict weed emergence and crop losses now and in the future.

The Weed Seed Wizard is a computer simulation tool that:

• applies to all Australian grain growing areas

• helps growers understand and manage weed seedbanks on their farms

• uses farm management records to simulate how different crop rotations, weed control techniques, irrigation, grazing and harvest management tactics can affect weed numbers, the weed seedbank and yields

• uses farm-specific management and site-specific weather

• is multi-species

See www.dpird.wa.gov.au for further information on Weed Seed Wizard.

This case …

Two Is Enough, But Three (Or More) Is Better: In Ai And Beyond, Olga Kosheleva, Vladik Kreinovich, Victor L. Timchenko, Yury P. Kondratenko

Departmental Technical Reports (CS)

At present, the most successful AI technique is deep learning -- the use of neural networks that consist of multiple layers. Interestingly, it is well known that neural networks with two data processing layers are sufficient -- in the sense that they can approximate any function with any given accuracy. Because of this, until reasonably recently, researchers and practitioners used such networks. However, recently it turned out, somewhat unexpectedly, that using three or more data processing layers -- i.e., using what is called deep learning -- makes the neural networks much more efficient. In this paper, on numerous examples from …

Tabular Data-Centric Ai: Challenges, Techniques And Future Perspectives, Yanjie Fu, Dongjie Wang, Hui Xiong, Kunpeng Liu

Computer Science Faculty Publications and Presentations

Tabular data are the most widely used data formats in almost every application domain, such as, biology, ecology, and material science. The purpose of tabular data-centric AI is to use AI to augment the predictive power of tabular data to get better AI. Tabular data-centric AI is essential because it can reconstruct distance measures, reshape discriminative patterns, and improve data AI readiness (structural, predictive, interaction, and expression levels), which is significant in industries and real-world deployments. Therefore, our tutorial is designed to capture the interest of professionals with expertise in artificial intelligence, machine learning, and data mining, as well as …

Quantifying Ethics And Trust In Human-Ai Collaboration, Oliver Lane, Kevin Klave

College of Engineering Summer Undergraduate Research Program

As AI Chatbots continue to evolve in both prevalence and capability, their role in education is becoming increasingly prominent. With chatbots like ChatGPT becoming commonplace in higher education, there is an evident need to understand the ethics and trust dynamics of human-AI collaboration. This research contributes to the ongoing discussion on AI in education, highlighting the importance of trust when utilizing AI in academic settings. By conducting an empirical analysis, this research seeks to quantify trust in human-AI collaboration in higher education with the aim of offering actionable items for higher education institutions to follow to promote ethical and responsible …

Identifying Neural Correlates Of Balance Impairment In Traumatic Brain Injury Using Partial Least Squares Correlation Analysis, Vikram Shenoy Handiru, Easter Selvan Suviseshamuthu, Soha Saleh, Haiyan Su, Guang Yue, Didier Allexandre

School of Computing Faculty Scholarship and Creative Works

Objective. Balance impairment is one of the most debilitating consequences of traumatic brain injury (TBI). To study the neurophysiological underpinnings of balance impairment, the brain functional connectivity during perturbation tasks can provide new insights. To better characterize the association between the task-relevant functional connectivity and the degree of balance deficits in TBI, the analysis needs to be performed on the data stratified based on the balance impairment. However, such stratification is not straightforward, and it warrants a data-driven approach. Approach. We conducted a study to assess the balance control using a computerized posturography platform in 17 individuals with TBI and …

Investigating Public Acceptance Of Responses To Public Emergencies: Based On Text Transparency And Empathy Sentiment Analysis, Xuefeng Zhang, Yelin Huang

Journal of Scientific Information Research

[Purpose/significance]Regarding the responses to public emergencies published by official agencies on social media, this study aims to measure information transparency and empathy in the response text, and further to investigate their influence on public acceptance of responses. [Method/process]This study used public emergency responses published on Sina Weibo, a Chinese popular social media, as data source. Through carefully collecting and filtering, we finally acquired 170 public emergency responses released from 2021 to 2023. Furthermore, by using methods of content analysis, manual coding, and natural language processing, we measured information transparency of responses from three aspects: information disclosure, information accuracy, and information …

Construction And Application Of Multi-Dimensional Portrait System For Green Technology Innovation Enterprises In China: Taking The Green Transportation Technology Field As An Example, Wenke Hao, Jianlin Yang, Lei Miao

Journal of Scientific Information Research

[Purpose/significance]By constructing and applying the multi-dimensional portrait system of green technology innovation enterprises in China, this paper aims to comprehensively understand the status quo, advantages and obstacles of enterprises in specific fields in green technology innovation, so as to provide scientific references and suggestions for relevant government departments and decision makers of enterprises. [Method/process]Based on resource based view and environmental dependence theory, we select the internal and external labels of enterprises, and designs a multi-dimensional label system to objectively describe the performance of enterprises in terms of profitability,scientific research and innovation, public opinion and environmental responsibility. Then, the green technology …

Ontology Design Facilitating Wikibase Integration — And A Worked Example For Historical Data, Cogan Shimizu, Andrew Eells, Seila Gonzalez, Lu Zhou, Pascal Hitzler, Alicia Sheill, Catherine Foley, Dean Rehberger

Computer Science and Engineering Faculty Publications

Wikibase – which is the software underlying Wikidata – is a powerful platform for knowledge graph creation and management. However, it has been developed with a crowd-sourced knowledge graph creation scenario in mind, which in particular means that it has not been designed for use case scenarios in which a tightly controlled high-quality schema, in the form of an ontology, is to be imposed, and indeed, independently developed ontologies do not necessarily map seamlessly to the Wikibase approach. In this paper, we provide the key ingredients needed in order to combine traditional ontology modeling with use of the Wikibase platform, …

Fail Fast, Fail Small: Designing Resilient Systems For The Future Of Software Engineering, Jill Willard, James Hutson

Faculty Scholarship

The principles of "fail fast, fail small" have emerged as critical in modern software and system design. By planning for minor, manageable failures instead of catastrophic breakdowns, developers can ensure that systems degrade gracefully, maintaining functionality even when encountering issues. This article delves into strategies for designing resilient systems, beginning with the concept of slow degradation and distributed systems that prioritize core functions while allowing non-critical components to fail without significant user impact. The Netflix recommendation engine serves as a prime example of a system that continues to operate under failure conditions. Chaos engineering, a proactive methodology for stress-testing system …

Research On Vector Database And Its Application, Yusheng Sun, Junhao Zeng

Journal of Scientific Information Research

[Purpose/significance]The article reveals the theoretical systems, technological systems, and applied systems of vector databases, aiming to promote innovation in the research and practice of multimodal AI related theories, technologies, and applications. [Method/process]This article elaborates on the evolution of vector databases and defines its core concepts through literatures tracing and content analyzing. Subsequently, it compares and analyzes their characteristics and values, and based on this, sorts out their application mechanisms, functions, corresponding key technologies and application modes. Simultaneously, it discusses the challenges and countermeasures faced by vector databases, and looks forward to their development trends from theoretical, technical, and application perspectives. …

Bibliography For "Ai: The Next Chapter Display", Arianna Tillman, Isabella Piechota

Library Displays and Bibliographies

A bibliography created to support a display about artificial intelligence at the Leatherby Libraries during Fall 2024 at the Leatherby Libraries at Chapman University.

Self-Supervised Learning For Time Series Analysis : Taxonomy, Progress, And Prospects, Zhang Kexin, Qingsong Wen, Chaoli Zhang, Rongyao Cai, Ming Jin, Yong Liu, James Y. Zhang, Guansong Pang, Guansong Pang, Pan Shirui

Research Collection School Of Computing and Information Systems

Self-supervised learning (SSL) has recently achieved impressive performance on various time series tasks. The most prominent advantage of SSL is that it reduces the dependence on labeled data. Based on the pre-training and fine-tuning strategy, even a small amount of labeled data can achieve high performance. Compared with many published self-supervised surveys on computer vision and natural language processing, a comprehensive survey for time series SSL is still missing. To fill this gap, we review current state-of-the-art SSL methods for time series data in this article. To this end, we first comprehensively review existing surveys related to SSL and time …

Generative Ai In Software Engineering Must Be Human-Centered: The Copenhagen Manifesto, D. Russo, S. Van Berkel Baltes, Christoph Treude

Research Collection School Of Computing and Information Systems

The advent of Generative Artificial Intelligence—systems that can produce human-like content such as text, music, visual art, or source code—marks not only a significant leap for Artificial Intelligence (AI) but also a pivotal moment for software practitioners and researchers. The role of software engineering researchers and practitioners in adopting the technologies that shape our world is critical. Historically, the human aspects of developing software have been treated as secondary to more technical innovations. However, the emergence of Generative AI will simultaneously enhance human capabilities while surfacing complex ethical, social, legal, and technical challenges.While primarily aimed at software engineering (SE) researchers …

Transformer-Based Joint Learning Approach For Text Normalization In Vietnamese Automatic Speech Recognition Systems, The Viet Bui, Tho Chi Luong, Oanh Thi Tran

Research Collection School Of Computing and Information Systems

In this article, we investigate the task of normalizing transcribed texts in Vietnamese Automatic Speech Recognition (ASR) systems in order to improve user readability and the performance of downstream tasks. This task usually consists of two main sub-tasks: predicting and inserting punctuation (i.e., period, comma); and detecting and standardizing named entities (i.e., numbers, person names) from spoken forms to their appropriate written forms. To achieve these goals, we introduce a complete corpus including of 87,700 sentences and investigate conditional joint learning approaches which globally optimize two sub-tasks simultaneously. The experimental results are quite promising. Overall, the proposed architecture outperformed the …

Documenting Ethical Considerations In Open Source Ai Models, Haoyu Gao, Mansooreh Zahedi, Christoph Treude, Sarita Rosenstock, Marc Cheong

Research Collection School Of Computing and Information Systems

Background: The development of AI-enabled software heavily depends on AI model documentation, such as model cards, due to different domain expertise between software engineers and model developers. From an ethical standpoint, AI model documentation conveys critical information on ethical considerations along with mitigation strategies for downstream developers to ensure the delivery of ethically compliant software. However, knowledge on such documentation practice remains scarce. Aims: The objective of our study is to investigate how developers document ethical aspects of open source AI models in practice, aiming at providing recommendations for future documentation endeavours. Method: We selected three sources of documentation on …

A Survey Of Unikernel Security: Insights And Trends From A Quantitative Analysis, Alex Wollman, John Hastings

Research & Publications

Unikernels, an evolution of LibOSs, are emerging as a virtualization technology to rival those currently used by cloud providers. Unikernels combine the user and kernel space into one ``uni''fied memory space and omit functionality that is not necessary for its application to run, thus drastically reducing the required resources. The removed functionality is significant however, and includes components that have become common security technologies such as Address Space Layout Randomization (ASLR), Data Execution Prevention (DEP), and Non-executable bits (NX bits). This raises questions about the security of unikernels. This research presents a quantitative methodology using TF-IDF to analyze the focus …

Solubility Characterization Of Organic Molecules For Aqueous Organic Redox Flow Batteries, Anthony W. Ferrell, Harkeerith K. Vij, Seamus D. Jones

College of Engineering Summer Undergraduate Research Program

The major obstacle to renewable energy sources is a lack of long-term energy storage capabilities. Energy produced during the day dissipates, leaving insufficient electricity for at night. The goal of the project is to design an Aqueous Organic Redox Flow Battery (AORFB) to act as long-term energy storage. Work has been done using machine learning to identify suitable compounds for the batteries. In this work there was no indication as to the aqueous solubility of the molecules; this controls the device’s energy storage capabilities. We used a machine learning model to determine the aqueous solubility of slightly more than 3000 …

Targeting Federated Learning: A Study Of Membership Inference Attacks On Healthcare Data, Brett W. Hillyard, Aditi S. Lappathi

College of Engineering Summer Undergraduate Research Program

This study investigates the vulnerabilities of federated learning models in the healthcare domain, specifically focusing on membership inference attacks (MIA). Federated learning allows local models to train on sensitive healthcare data without sharing the data itself, making it an attractive method for protecting privacy. However, even in this decentralized framework, models remain vulnerable to MIAs, where attackers can infer whether certain data points were used to train a model by analyzing model updates. Using the Texas100 dataset, this study demonstrates that as the number of local models increases, the attack accuracy of MIAs also increases due to higher bias …

Fast And High-Resolution View Synthesis From A Single Input Panorama, Nam Nguyen, Angela V. Chen, Theresa Zhu, Seth Johnson, Pranav Dumpa, Benjamin Geil

College of Engineering Summer Undergraduate Research Program

We introduce a novel method to convert a single input panorama into a 3D colored mesh representation of the scene. Unlike recent methods based on neural rendering, which are limited to low-resolution inputs and offline rendering, our approach supports 4k resolution inputs and real time rendering in a virtual reality headset. We first estimate a depth map and produce an initial layered depth image (LDI) representation. We fill unseen regions behind objects by iteratively cutting and inpainting the LDI. We then convert the LDI into an optimized, texture mapped mesh to achieve a compact representation