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Full-Text Articles in Entire DC Network
Stability Of Predator-Prey Model For Worm Attack In Wireless Sensor Networks, Rajeev Kishore, Padam Singh
Stability Of Predator-Prey Model For Worm Attack In Wireless Sensor Networks, Rajeev Kishore, Padam Singh
Applications and Applied Mathematics: An International Journal (AAM)
In this paper, we propose a predator-prey mathematical model for analyzing the dynamical behaviors of the system. This system is an epidemic model, and it is capable of ascertaining the worm's spreading at the initial stage and improving the security of wireless sensor networks. We investigate different fixed points and examine the stability of the projected model.
Developing Singapore As A Smart Nation, Josephine Teo
Developing Singapore As A Smart Nation, Josephine Teo
Asian Management Insights
Mrs Josephine Teo, Singapore’s Minister for Communications and Information, and Minister-in-charge of Smart Nation and Cybersecurity, speaks about leading the country’s Smart Nation drive.
Technical Note: Generalizable And Promptable Artificial Intelligence Model To Augment Clinical Delineation In Radiation Oncology, Lian Zhang, Zhengliang Liu, Lu Zhang, Zihao Wu, Xiaowei Yu, Jason Holmes, Hongying Feng, Haixing Dai, Xiang Li, Quanzheng Li, William W. Wong, Sujay A. Vora, Dajiang Zhu, Tianming Liu, Wei Liu
Technical Note: Generalizable And Promptable Artificial Intelligence Model To Augment Clinical Delineation In Radiation Oncology, Lian Zhang, Zhengliang Liu, Lu Zhang, Zihao Wu, Xiaowei Yu, Jason Holmes, Hongying Feng, Haixing Dai, Xiang Li, Quanzheng Li, William W. Wong, Sujay A. Vora, Dajiang Zhu, Tianming Liu, Wei Liu
Computer Science Faculty Research & Creative Works
Background: Efficient and accurate delineation of organs at risk (OARs) is a critical procedure for treatment planning and dose evaluation. Deep learning-based auto-segmentation of OARs has shown promising results and is increasingly being used in radiation therapy. However, existing deep learning-based auto-segmentation approaches face two challenges in clinical practice: generalizability and human-AI interaction. A generalizable and prompt able auto-segmentation model, which segments OARs of multiple disease sites simultaneously and supports on-the-fly human-AI interaction, can significantly enhance the efficiency of radiation therapy treatment planning. Purpose: Meta's segment anything model (SAM) was proposed as a generalizable and prompt able model for next-generation …
Towards Understanding Convergence And Generalization Of Adamw, Pan Zhou, Xingyu Xie, Zhouchen Lin, Shuicheng Yan
Towards Understanding Convergence And Generalization Of Adamw, Pan Zhou, Xingyu Xie, Zhouchen Lin, Shuicheng Yan
Research Collection School Of Computing and Information Systems
AdamW modifies Adam by adding a decoupled weight decay to decay network weights per training iteration. For adaptive algorithms, this decoupled weight decay does not affect specific optimization steps, and differs from the widely used ℓ2-regularizer which changes optimization steps via changing the first- and second-order gradient moments. Despite its great practical success, for AdamW, its convergence behavior and generalization improvement over Adam and ℓ2-regularized Adam (ℓ2-Adam) remain absent yet. To solve this issue, we prove the convergence of AdamW and justify its generalization advantages over Adam and ℓ2-Adam. Specifically, AdamW provably converges but minimizes a dynamically regularized loss that …
Non-Monotonic Generation Of Knowledge Paths For Context Understanding, Pei-Chi Lo, Ee-Peng Lim
Non-Monotonic Generation Of Knowledge Paths For Context Understanding, Pei-Chi Lo, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Knowledge graphs can be used to enhance text search and access by augmenting textual content with relevant background knowledge. While many large knowledge graphs are available, using them to make semantic connections between entities mentioned in the textual content remains to be a difficult task. In this work, we therefore introduce contextual path generation (CPG) which refers to the task of generating knowledge paths, contextual path, to explain the semantic connections between entities mentioned in textual documents with given knowledge graph. To perform CPG task well, one has to address its three challenges, namely path relevance, incomplete knowledge graph, and …
Analyzing Biomedical Datasets With Symbolic Tree Adaptive Resonance Theory, Sasha Petrenko, Daniel B. Hier, Mary A. Bone, Tayo Obafemi-Ajayi, Erik J. Timpson, William E. Marsh, Michael Speight, Donald C. Wunsch
Analyzing Biomedical Datasets With Symbolic Tree Adaptive Resonance Theory, Sasha Petrenko, Daniel B. Hier, Mary A. Bone, Tayo Obafemi-Ajayi, Erik J. Timpson, William E. Marsh, Michael Speight, Donald C. Wunsch
Chemistry Faculty Research & Creative Works
Biomedical Datasets Distill Many Mechanisms Of Human Diseases, Linking Diseases To Genes And Phenotypes (Signs And Symptoms Of Disease), Genetic Mutations To Altered Protein Structures, And Altered Proteins To Changes In Molecular Functions And Biological Processes. It Is Desirable To Gain New Insights From These Data, Especially With Regard To The Uncovering Of Hierarchical Structures Relating Disease Variants. However, Analysis To This End Has Proven Difficult Due To The Complexity Of The Connections Between Multi-Categorical Symbolic Data. This Article Proposes Symbolic Tree Adaptive Resonance Theory (START), With Additional Supervised, Dual-Vigilance (DV-START), And Distributed Dual-Vigilance (DDV-START) Formulations, For The Clustering Of …
Fixing Your Own Smells: Adding A Mistake-Based Familiarization Step When Teaching Code Refactoring, Ivan Wei Han Tan, Christopher M. Poskitt
Fixing Your Own Smells: Adding A Mistake-Based Familiarization Step When Teaching Code Refactoring, Ivan Wei Han Tan, Christopher M. Poskitt
Research Collection School Of Computing and Information Systems
Programming problems can be solved in a multitude of functionally correct ways, but the quality of these solutions (e.g. readability, maintainability) can vary immensely. When code quality is poor, symptoms emerge in the form of 'code smells', which are specific negative characteristics (e.g. duplicate code) that can be resolved by applying refactoring patterns. Many undergraduate computing curricula train students on this software engineering practice, often doing so via exercises on unfamiliar instructor-provided code. Our observation, however, is that this makes it harder for novices to internalise refactoring as part of their own development practices. In this paper, we propose a …
Active Discovering New Slots For Task-Oriented Conversation, Yuxia Wu, Tianhao Dai, Zhedong Zheng, Lizi Liao
Active Discovering New Slots For Task-Oriented Conversation, Yuxia Wu, Tianhao Dai, Zhedong Zheng, Lizi Liao
Research Collection School Of Computing and Information Systems
Existing task-oriented conversational systems heavily rely on domain ontologies with pre-defined slots and candidate values. In practical settings, these prerequisites are hard to meet, due to the emerging new user requirements and ever-changing scenarios. To mitigate these issues for better interaction performance, there are efforts working towards detecting out-of-vocabulary values or discovering new slots under unsupervised or semi-supervised learning paradigms. However, overemphasizing on the conversation data patterns alone induces these methods to yield noisy and arbitrary slot results. To facilitate the pragmatic utility, real-world systems tend to provide a stringent amount of human labeling quota, which offers an authoritative way …
T-Pickseer: Visual Analysis Of Taxi Pick-Up Point Selection Behavior, Shuxian Gu, Yemo Dai, Zezheng Feng, Yong Wang, Haipeng Zeng
T-Pickseer: Visual Analysis Of Taxi Pick-Up Point Selection Behavior, Shuxian Gu, Yemo Dai, Zezheng Feng, Yong Wang, Haipeng Zeng
Research Collection School Of Computing and Information Systems
Taxi drivers often take much time to navigate the streets to look for passengers, which leads to high vacancy rates and wasted resources. Empty taxi cruising remains a big concern for taxi companies. Analyzing the pick-up point selection behavior can solve this problem effectively, providing suggestions for taxi management and dispatch. Many studies have been devoted to analyzing and recommending hotspot regions of pick-up points, which can make it easier for drivers to pick-up passengers. However, the selection of pick-up points is complex and affected by multiple factors, such as convenience and traffic management. Most existing approaches cannot produce satisfactory …
A Study On Ethical Hacking In Cybersecurity Education Within The United States, Jordan Chew
A Study On Ethical Hacking In Cybersecurity Education Within The United States, Jordan Chew
Master's Theses
As the field of computer security continues to grow, it becomes increasingly important to educate the next generation of security professionals. However, much of the current education landscape primarily focuses on teaching defensive skills. Teaching offensive security, otherwise known as ethical hacking, is an important component in the education of all students who hope to contribute to the field of cybersecurity. Doing so requires a careful consideration of what ethical, legal, and practical issues arise from teaching students skills that can be used to cause harm. In this thesis, we first examine the current state of cybersecurity education in the …
Improving Automatic Transcription Using Natural Language Processing, Anna Kiefer
Improving Automatic Transcription Using Natural Language Processing, Anna Kiefer
Master's Theses
Digital Democracy is a CalMatters and California Polytechnic State University initia-
tive to promote transparency in state government by increasing access to the Califor-
nia legislature. While Digital Democracy is made up of many resources, one founda-
tional step of the project is obtaining accurate, timely transcripts of California Senate
and Assembly hearings. The information extracted from these transcripts provides
crucial data for subsequent steps in the pipeline. In the context of Digital Democracy,
upleveling is when humans verify, correct, and annotate the transcript results after
the legislative hearings have been automatically transcribed. The upleveling process
is done with the …
Enhancing The Resilience Of Space Systems Against Ransomware Attacks, Petersen F. Hansen
Enhancing The Resilience Of Space Systems Against Ransomware Attacks, Petersen F. Hansen
Theses and Dissertations
As their relevance has increased in recent years, space systems have become nearly essential in modern life. They are integral in the operation of navigational systems, military operations, and have ushered in a new domain of scientific inquiry. Technological advances have enabled the miniaturization of components and increased the accessibility of satellites as they find new applications in the form of Cube Satellites. However, even as these advancements have brought satellites to new heights, their interconnectedness leaves them open to new cyber threats. Ransomware attacks are one of the most prominent and disruptive cyber threats to terrestrial systems, and while …
Measuring The Presentation Of Supporting Content For A Set Of Learning Objectives Throughout The States Of An Educational Game Tree, Michael L. Hastriter Jr.
Measuring The Presentation Of Supporting Content For A Set Of Learning Objectives Throughout The States Of An Educational Game Tree, Michael L. Hastriter Jr.
Theses and Dissertations
In an era of evolving warfare, the Department of Defense (DoD) recognizes the value of serious games as immersive tools for teaching critical concepts. This thesis introduces a pioneering framework tailored to enhance learning objectives through the presentation of educational game content. This addresses the unique needs of the DoD and other educators who use games by providing measurements to assess educational games. This researches investigates how instructors might assess games as potential teaching tools. It establishes a five-phase process, providing a framework to assess educational games against predefined learning objectives and informing future game development. This thesis demonstrates games …
Design Considerations For The Use Of The Julia Programming Language In Future Quantum Networking Simulation Software, Takashi Joubert
Design Considerations For The Use Of The Julia Programming Language In Future Quantum Networking Simulation Software, Takashi Joubert
Theses and Dissertations
Given the prevalence of Python-based packages in the existing quantum network simulation ecosystem, we attempt to assess what might be realistically gained by switching to Julia. We focus our experimental activities on three areas: 1) surveying the characteristics of Julia as they tie into robust framework development, 2) presenting benchmarks that compare Julia and Python with respect to elements of possible simulation workloads, and 3) producing a tangible lightweight Julia architecture for modeling components in a manner similar to SeQUeNCe. Our analysis suggests that while Julia does o.er performance advantages over Python over certain workloads, knowing the reasons for why …
Malware Detection And Signature Propagation: A Study On Anti-Virus Platforms, Aaron J. Morath
Malware Detection And Signature Propagation: A Study On Anti-Virus Platforms, Aaron J. Morath
Theses and Dissertations
The early detection of malware across DoD networks is paramount when considering which AV engine to employ. This study explores malware detection latency across various AV providers over a 30-day period using VirusTotal’s platform. The analysis reveals an initial surge in detections, reaching approximately 60% within 24 hours. From days 3 to 20, detections steadily increase by 1-3 instances per day, peaking at 74% on the 20th day, followed by a slight decline. The research also highlights a significant difference in false positive rates between packed and non-packed non-malicious samples, emphasizing the impact of packing on AV engine scans. While …
Enhancing Sequence With Quantum Key Distribution Protocols And An Intuitive User Interface, Blake Perkins
Enhancing Sequence With Quantum Key Distribution Protocols And An Intuitive User Interface, Blake Perkins
Theses and Dissertations
The rapidly growing domain of quantum networks necessitates advancements in associated software packages. This master’s thesis will detail, in part, new protocols added to extend the usefulness of SeQUeNCe. Notably, these added protocols were implemented to ensure compatibility and efficiency with the existing codebase. To complement this expansion in capability, the graphical user interface (GUI) was restructured. Updates to the GUI now allow users to initiate and operate these newly integrated protocols with ease, thereby expanding the accessibility of SeQUeNCe to a wider audience. By prioritizing the incorporation of these new protocols and refining the user interface, this research significantly …
Opportunities And Limitations: Integrating Narrative Ai Into Game-Based Assessment Creation And Evaluating Student Impacts, Kevin B. Patterson
Opportunities And Limitations: Integrating Narrative Ai Into Game-Based Assessment Creation And Evaluating Student Impacts, Kevin B. Patterson
Theses and Dissertations
The Department of Defense (DoD) has identified the need for a technically proficient workforce in the areas of science, technology, engineering, and mathematics. To meet this need, the DoD is actively seeking innovative technology capable of creating workforce development opportunities that are both accessible and effective. Educational research indicates serious games provide a potential avenue to achieve this goal. Unfortunately, a limited number of tools that simplify the game development process and leverage artificial intelligence are available. Content-generating artificial intelligence might help reduce instructor and game-based assessment designers' workloads while promoting individualized learning in students. This research presents a novel …
Optimization Of Bluetooth Auracast Broadcast Audio Transmissions Via Signal Modeling, Lauren J. Puseman
Optimization Of Bluetooth Auracast Broadcast Audio Transmissions Via Signal Modeling, Lauren J. Puseman
Theses and Dissertations
Since 1994, Bluetooth has been used as a Personal Area Network (PAN) to transfer data between devices within a short range. After thirty years of progressive improvement in functionality, security, range, and power efficiency, the Bluetooth Special Interest Group has released a new feature called Auracast, which allows users to tune into nearby public audio streams and receive the feed directly to their wireless headphones or hearing aids. Soon, venues such as convention centers, museums, public forums, and sporting arenas can implement Auracast to better suit their needs. In addition to these public benefits, the Department of Defense can take …
Group Convolutional Decoders For Toric Codes, Jim Wang
Group Convolutional Decoders For Toric Codes, Jim Wang
Theses and Dissertations
Quantum Error Correction (QEC) enables both industrial and defense applications of quantum computing. Toric codes and other quantum Low-Density Parity-Check (LDPC) codes are promising and well-researched methods of QEC. However, their decoding cost increases exponentially with a computer’s qubit count. Neural Network (NN) decoders have been shown to decode a code’s error syndrome both accurately and fast enough for a real-time error correcting scheme. Recent key developments introduced Convolutional Neural Network (CNN) to implement a translationally equivariant decoder for a toric code. These CNN decoders both outperform NN decoders and require less training data. This research applies a Group Convolutional …
Quantum Circuit Reduction Using Three Layer Transposition, Christian L. Grauberger
Quantum Circuit Reduction Using Three Layer Transposition, Christian L. Grauberger
Theses and Dissertations
The potential of quantum computing to revolutionize critical military applications has led the US Department of Defense to recognize it as a keen interest. However, the practical implementation of these theoretical applications on physical quantum devices is currently limited by inherent reliability and accuracy issues in quantum hardware. To mitigate errors stemming from these limitations, the incorporation of software-based solutions is imperative. Quantum circuit optimization stands out as a primary method of increasing the accuracy of quantum computations. One of the key components of this approach is circuit reduction, whereby circuits are condensed to realize the same computation using fewer …
Modeling & Engineering Of Usmepcom Business Intelligence Data, Merrick A. Bedford
Modeling & Engineering Of Usmepcom Business Intelligence Data, Merrick A. Bedford
Theses and Dissertations
This thesis investigates the USMEPCOM’s issue of modeling and engineering Business Intelligence data centralized around the MEPS of Excellence (MOE) program. MEPS around the US conduct military personnel in-processing and in doing so have a vested interest in the standardization and application of the data associated with such processes. There are currently 65 MEPS stations and one RPS that handle military personnel onboarding paperwork and make determinations for military eligibility. This topic is important due to the MEPS cloud data processing system modernization efforts requiring data processing adaptation to ensure relevant and meaningful usage of current data.
Reinforcement Learning For Team Based Air Combat Maneuvering Decisions With Directed Energy Weaponry, Joshua D. Combs
Reinforcement Learning For Team Based Air Combat Maneuvering Decisions With Directed Energy Weaponry, Joshua D. Combs
Theses and Dissertations
Leveraging the Advanced Framework for Simulation, Integration, and Modeling (AFSIM) we investigate the use of reinforcement learning (RL) techniques for imbuing AUCAV agents with high-quality behaviors for the within-visual-range air combat maneuvering problem (ACMP). We formulate the 2v2 WVR ACMP as a Markov decision process wherein friendly AUCAVs are equipped with DEW capabilities and operate with 6 degrees of freedom. We utilize the Double Deep Q-Network RL algorithm, which centrally trains two friendly AUCAVs and employ a phased learning approach, initially exposing the AUCAVs to a dense reward environment for early training, followed by a sparse reward environment to encourage …
U.S. Army Cadet Command Branch Prediction Model, Daniel M. Krizan
U.S. Army Cadet Command Branch Prediction Model, Daniel M. Krizan
Theses and Dissertations
The current system for providing US Army ROTC cadets their branches leaves significant uncertainty until the final pronouncement of branch assigned. This uncertainty can be alleviated by providing a prediction model for cadets to input personal data and desired branch to identify likelihood of receiving the request. This thesis produces a machine learning model capable of producing branch prediction for cadets.
Federated Analysis Of Wearables Data For United States Air Force Mental And Physical Readiness, Sung Yong O
Federated Analysis Of Wearables Data For United States Air Force Mental And Physical Readiness, Sung Yong O
Theses and Dissertations
This thesis investigates the use of machine learning and deep learning models within a federated learning framework to predict physical and mental readiness in military personnel, using wearable technology data. The collaboration with the 711th Human Performance Wing’s STRONG Lab highlights the importance of readiness as emphasized by the National Defense and Security Strategies. The study evaluates various predictive models, incorporating federated learning to ensure data privacy and security in healthcare systems. By analyzing a comprehensive dataset, the research aims to contribute to military readiness enhancement through technological advancements, supporting health and wellness initiatives to bolster the effectiveness of military …
A Reinforcement Learning Approach To The 2v2 Beyond Visual Range Air Combat Maneuvering Problem, Jacob J. Pike
A Reinforcement Learning Approach To The 2v2 Beyond Visual Range Air Combat Maneuvering Problem, Jacob J. Pike
Theses and Dissertations
This research examines a 2v2 air combat maneuvering problem (ACMP) in a Beyond Visual Range (BVR) environment. A discrete-time, infinite-horizon Markov Decision Process (MDP) model represents the BVR-ACMP, seeking to determine high-quality policies for a pair of autonomous aircraft to execute tactical maneuvers and firing decisions. The Advanced Framework for Simulation, Integration, and Modeling (AFSIM) characterizes the complex six-degree of freedom (6-DOF) aircraft operations, encompassing kinematics, sensors, and weapons. Given the high dimensionality and continuous nature of the state and decision variables, a deep reinforcement learning (RL) solution approach is adopted wherein the value function is approximated via a Neural …
A Random Forest-Based Q-Learning Algorithm: Toward Interpretable Artificial Intelligence, Victor R. Rae
A Random Forest-Based Q-Learning Algorithm: Toward Interpretable Artificial Intelligence, Victor R. Rae
Theses and Dissertations
A growing demand exists for interpretable artificial intelligence models, leading to extensive research efforts to enhance the explainability and transparency of policies generated by reinforcement learning (RL) methods. This research develops random forest-based RL algorithms as a logical progression in this academic pursuit. The algorithms are evaluated using three standard benchmark environments from OpenAI gym — CartPole, MountainCar, and LunarLander — and compared to implementations of the Deep Q-learning Network (DQN) and Double DQN (DDQN) algorithms for various metrics, including performance, robustness, efficiency, and interpretability. The random forest-based algorithms exhibit superior performance to both neural network-based algorithms in two out …
Artificial Intelligence And Perception: An Empirical Study, Anthony J. Neil
Artificial Intelligence And Perception: An Empirical Study, Anthony J. Neil
Theses and Dissertations
This thesis investigates the impact of adjusting artificial intelligence explainability levels’ outputs on user perception. The overarching study extends within the Explainable Artificial Intelligence (XAI) domain. It examines a spectrum of variables, including performance, cognizance, familiarity, transparency, system bias, and the overall impact of AI, to understand their collective and individual effects that enable effective professional use in an organization. The study aims to illuminate the relationship between the degree of explainability provided by large language models such as ChatGPT, Bard, and Bing AI and the performance of these models when tasked with XAI adjustments.
A Model-Based And System-Theoretic Approach To Design Advanced Autonomy For Air Dominance Missions: A Loyal Wingman Case Study, Elizabeth S. Pennington
A Model-Based And System-Theoretic Approach To Design Advanced Autonomy For Air Dominance Missions: A Loyal Wingman Case Study, Elizabeth S. Pennington
Theses and Dissertations
In contested air environments, safe coordination between decision-makers is paramount. Although the Department of Defense (DoD) prioritizes the development of Artificially Intelligent (AI) wingmen for air combat, a lack of methodology exists to design safe, holistic coordination between human and autonomous wingmen in the same environment. This thesis delivers a framework using Systems Theoretic Process Analysis Extended for Coordination (STPA-Coord) to analyze and design holistic coordination for the Loyal Wingman concept in an Air Dominance mission. STPA-Coord is a safety and hazard analysis process that uses Systems Theory to analyze and design coordination between decisionmakers in a system-of-systems architecture. Using …
Standardization Of Risk Classifications For Unmanned Space Vehicle Missions, Collin A. Gwaltney
Standardization Of Risk Classifications For Unmanned Space Vehicle Missions, Collin A. Gwaltney
Theses and Dissertations
This paper seeks to model risk classification levels (A-D) for 122 Space Vehicle programs. Models include multinomial logistic regression as well as random forest, a machine learning technique based on decision trees. We use independent variables (IVs) which are theoretically correlated to risk class for the regression and one random forest model. We then include all IVs and allow the random forest technique to use those which provide the most information on risk class before paring down the number of IVs to only 7. We show that the accuracy of predictions increases from 62% to 87% by using random forest …
Signal-To-Image Method For Counterfeit Detection In Layered Security Paradigm, Jordan Williamson
Signal-To-Image Method For Counterfeit Detection In Layered Security Paradigm, Jordan Williamson
Theses and Dissertations
National level attention, resources, and priority regarding critical infrastructure have increased in recent years. This has led to adversaries and defenders exchanging positions between fortification and exploitation. One area that continues to be vulnerable is supply chain attacks like counterfeit insertion. This work investigates the application of converting collected signals into images from devices that may be considered for these critical networks. There are several aspects regarding the conversion of signals into images - specifically Red, Green, Blue (RGB) images. The methodology proposed here is potentially ideal fit for an initial security layer by achieving comparable classification results as more …