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Articles 15241 - 15270 of 63040

Full-Text Articles in Computer Sciences

Neuron Coverage-Guided Domain Generalization, Chris Xing Tian, Haoliang Li, Xiaofei Xie, Yang Liu, Shiqi Wang Mar 2022

Neuron Coverage-Guided Domain Generalization, Chris Xing Tian, Haoliang Li, Xiaofei Xie, Yang Liu, Shiqi Wang

Research Collection School Of Computing and Information Systems

This paper focuses on the domain generalization task where domain knowledge is unavailable, and even worse, only samples from a single domain can be utilized during training. Our motivation originates from the recent progresses in deep neural network (DNN) testing, which has shown that maximizing neuron coverage of DNN can help to explore possible defects of DNN (i.e.,misclassification). More specifically, by treating the DNN as a program and each neuron as a functional point of the code, during the network training we aim to improve the generalization capability by maximizing the neuron coverage of DNN with the gradient similarity regularization …

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Towards Efficient Annotations For A Human-Ai Collaborative, Clinical Decision Support System: A Case Study On Physical Stroke Rehabilitation Assessment, Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez I Badia Mar 2022

Towards Efficient Annotations For A Human-Ai Collaborative, Clinical Decision Support System: A Case Study On Physical Stroke Rehabilitation Assessment, Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez I Badia

Research Collection School Of Computing and Information Systems

Artificial intelligence (AI) and machine learning (ML) algorithms are increasingly being explored to support various decision-making tasks in health (e.g. rehabilitation assessment). However, the development of such AI/ML-based decision support systems is challenging due to the expensive process to collect an annotated dataset. In this paper, we describe the development process of a human-AI collaborative, clinical decision support system that augments an ML model with a rule-based (RB) model from domain experts. We conducted its empirical evaluation in the context of assessing physical stroke rehabilitation with the dataset of three exercises from 15 post-stroke survivors and therapists. Our results bring …

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Debiasing Nlu Models Via Causal Intervention And Counterfactual Reasoning, Bing Tian, Yixin Cao, Yong Zhang, Chunxiao Xing Mar 2022

Debiasing Nlu Models Via Causal Intervention And Counterfactual Reasoning, Bing Tian, Yixin Cao, Yong Zhang, Chunxiao Xing

Research Collection School Of Computing and Information Systems

Recent studies have shown that strong Natural Language Understanding (NLU) models are prone to relying on annotation biases of the datasets as a shortcut, which goes against the underlying mechanisms of the task of interest. To reduce such biases, several recent works introduce debiasing methods to regularize the training process of targeted NLU models. In this paper, we provide a new perspective with causal inference to fnd out the bias. On the one hand, we show that there is an unobserved confounder for the natural language utterances and their respective classes, leading to spurious correlations from training data. To remove …

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Real Time Evaluation Of Boom And Drogue Occlusion With Aar, Xiaoyang Wu Mar 2022

Real Time Evaluation Of Boom And Drogue Occlusion With Aar, Xiaoyang Wu

Theses and Dissertations

In recent years, Unmanned Aerial Vehicles (UAV) have seen a rise in popularity. Various navigational algorithms have been developed as a solution to estimate a UAV’s pose relative to the refueler aircraft. The result can be used to safely automate aerial refueling (AAR) to improve UAVs’ time-on-station and ensure the success of military operations. This research aims to reach real-time performance using a GPU accelerated approach. It also conducts various experiments to quantify the effects of refueling boom/drogue occlusion and image exposure on the pose estimation pipeline in a lab setting.

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Analysis Of Generalized Artificial Intelligence Potential Through Reinforcement And Deep Reinforcement Learning Approaches, Jonathan Turner Mar 2022

Analysis Of Generalized Artificial Intelligence Potential Through Reinforcement And Deep Reinforcement Learning Approaches, Jonathan Turner

Theses and Dissertations

Artificial Intelligence is the next competitive domain; the first nation to develop human level artificial intelligence will have an impact similar to the development of the atomic bomb. To maintain the security of the United States and her people, the Department of Defense has funded research into the development of artificial intelligence and its applications. This research uses reinforcement learning and deep reinforcement learning methods as proxies for current and future artificial intelligence agents and to assess potential issues in development. Agent performance were compared across two games and one excursion: Cargo Loading, Tower of Hanoi, and Knapsack Problem, respectively. …

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Applying Models Of Circadian Stimulus To Explore Ideal Lighting Configurations, Alexander J. Price Mar 2022

Applying Models Of Circadian Stimulus To Explore Ideal Lighting Configurations, Alexander J. Price

Theses and Dissertations

Increased levels of time are spent indoors, decreasing human interaction with nature and degrading photoentrainment, the synchronization of circadian rhythms with daylight variation. Military imagery analysts, among other professionals, are required to work in low light level environments to limit power consumption or increase contrast on display screens to improve detail detection. Insufficient exposure to light in these environments results in inadequate photoentrainment which is associated with degraded alertness and negative health effects. Recent research has shown that both the illuminance (i.e., perceived intensity) and wavelength of light affect photoentrainment. Simultaneously, modern lighting technologies have improved our ability to construct …

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Evaluating Secure Enclave Firmware Development For Contemporary Risc-V Workstations, Samuel D. Chadwick Mar 2022

Evaluating Secure Enclave Firmware Development For Contemporary Risc-V Workstations, Samuel D. Chadwick

Theses and Dissertations

The emergence of the open-source RISC-V ISA empowers developers and engineers, device manufactures, industry leaders, nation-states, adversaries and allies alike with the unique opportunity to re-evaluate existing Trusted Computing paradigms. Emerging open-source security mechanisms facilitate the proliferation of Confidential Computing principles. These technology standards aim to provide secure enclave computing as a fundamental computing attribute, inherent within the RISC-V ISA specification. Security enforcement within these enclaves are handled by performing computation in memory-isolated, hardware-based, software-defined TEEs. This research evaluates the firmware development procedures required to implement Keystone Enclave on new unsupported hardware. Expressly, this effort extends Keystone SM firmware components …

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Malware Detection Using Electromagnetic Side-Channel Analysis, Matthew A. Bergstedt Mar 2022

Malware Detection Using Electromagnetic Side-Channel Analysis, Matthew A. Bergstedt

Theses and Dissertations

Many physical systems control or monitor important applications without the capacity to monitor for malware using on-device resources. Thus, it becomes valuable to explore malware detection methods for these systems utilizing external or off-device resources. This research investigates the viability of employing EM SCA to determine whether a performed operation is normal or malicious. A Raspberry Pi 3 was set up as a simulated motor controller with code paths for a normal or malicious operation. While the normal path only calculated the motor speed before updating the motor, the malicious path added a line of code to modify the calculated …

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Evaluating Semantic Matching Techniques For Technical Documents, Rain F. Dartt Mar 2022

Evaluating Semantic Matching Techniques For Technical Documents, Rain F. Dartt

Theses and Dissertations

Machine learning models that employ NLP techniques have become more widely accessible, making them an attractive solution for text and document classification tasks traditionally accomplished by humans. Two such use cases are matching the specialized experience required for a job to statements in applicant resumes, and finding and labelling clauses in legal contracts The AFMC has an immediate need for solutions to civilian hiring. However, there is currently no truth data to validate against. A similar task is contract understanding for which there is the CUAD, a recently published repository of 510 contracts manually labelled by legal experts. The presented …

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Exploiting The Iot Through Network-Based Covert Channels, Kyle S. Harris Mar 2022

Exploiting The Iot Through Network-Based Covert Channels, Kyle S. Harris

Theses and Dissertations

Information leaks are a top concern to industry and government leaders. The IoT is a technology capable of sensing real-world events. A method for exfiltrating data from these devices is by covert channel. This research designs a novel IoT CTC without the need for inter-packet delays to encode data. Instead, it encodes data within preexisting network information, namely ports or addresses. Additionally, the CTC can be implemented in two different modes: Stealth and Bandwidth. Performance is measured using throughput and detectability. The Stealth methods mimic legitimate traffic captures while the Bandwidth methods forgo this approach for maximum throughput. Detection results …

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Improving Anonymized Search Relevance With Natural Language Processing And Machine Learning, Niko A. Petrocelli Mar 2022

Improving Anonymized Search Relevance With Natural Language Processing And Machine Learning, Niko A. Petrocelli

Theses and Dissertations

Users often sacrifice personal data for more relevant search results, presenting a problem to communities that desire both search anonymity and relevant results. To balance these priorities, this research examines the impact of using Siamese networks to extend word embeddings into document embeddings and detect similarities between documents. The predicted similarity can locally re-rank search results provided from various sources. This technique is leveraged to limit the amount of information collected from a user by a search engine. A prototype is produced by applying the methodology in a real-world search environment. The prototype yielded an additional function of finding new …

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Investigating Collaboration In Software Reverse Engineering, Allison M. Wong Mar 2022

Investigating Collaboration In Software Reverse Engineering, Allison M. Wong

Theses and Dissertations

Reverse engineering (RE) is a rigorous process of exploration and analysis to support software design recovery and exploit development. The process is often conducted in teams to divide the workload and take full advantage of engineers' individual expertise and strengths. Collaboration in RE requires versatile and reliable tools that can match the environment's unpredictable and fluid nature. While studies on collaborative software development have indicated common best practices and implementations, similar standards have not been explored in reverse engineering. This research conducts semi-structured interviews with reverse engineering experts to understand their needs and solutions while working in a team. The …

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Smoothing Of Convolutional Neural Network Classifications, Glen R. Drumm Mar 2022

Smoothing Of Convolutional Neural Network Classifications, Glen R. Drumm

Theses and Dissertations

Smoothing convolutional neural networks is investigated. When intermittent and random false predictions happen, a technique of average smoothing is applied to smooth out the incorrect predictions. While a simple problem environment shows proof of concept, obstacles remain for applying such a technique to a more operationally complex problem.

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Approximate Dynamic Programming For An Unmanned Aerial Vehicle Routing Problem With Obstacles And Stochastic Target Arrivals, Kassie M. Gurnell Mar 2022

Approximate Dynamic Programming For An Unmanned Aerial Vehicle Routing Problem With Obstacles And Stochastic Target Arrivals, Kassie M. Gurnell

Theses and Dissertations

The United States Air Force is investing in artificial intelligence (AI) to speed analysis in efforts to modernize the use of autonomous unmanned combat aerial vehicles (AUCAVs) in strike coordination and reconnaissance (SCAR) missions. This research examines an AUCAVs ability to execute target strikes and provide reconnaissance in a SCAR mission. An orienteering problem is formulated as anMarkov decision process (MDP) model wherein a single AUCAV must optimize its target route to aid in eliminating time-sensitive targets and collect imagery of requested named areas of interest while evading surface-to-air missile (SAM) battery threats imposed as obstacles. The AUCAV adjusts its …

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An Investigation Of Data Storage In Entity-Component Systems, Bailey V. Compton Mar 2022

An Investigation Of Data Storage In Entity-Component Systems, Bailey V. Compton

Theses and Dissertations

Entity-Component Systems (ECS) have grown vastly in application since their introduction more than 20 years ago. Providing the ability to efficiently manage data and optimize program execution, ECSs, as well as the wider field of data-oriented design, have attained popularity in the realms of modeling, simulation, and gaming. This manuscript aims to elucidate and document the storage frameworks commonly found in ECSs, as well as suggesting conceptual connections between ECSs and relational databases. This formal documentation of the in-memory storage formats of entity-component systems affords the United States Air Force, the Department of Defense, and the software engineering community a …

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Autonomous And Resilient Management Of All-Source Sensors For Navigation Integrity: A Comparison And Analysis, Niles A. Tate Mar 2022

Autonomous And Resilient Management Of All-Source Sensors For Navigation Integrity: A Comparison And Analysis, Niles A. Tate

Theses and Dissertations

When navigating using Global Navigation Satellite Systems (GNSS), multiple/redundant, synchronous pseudorange measurements are readily available. However, when navigating in a GNSS degraded and/or denied region, this is not guaranteed. In response to this challenge, the ANT Center developed a framework known as Autonomous and Resilient Management of All-source Sensors (ARMAS). The ARMAS framework is designed to be resilient towards data corruption caused from mismodeled, uncalibrated, and faulty sensors. This thesis further expands on this work by performing a comparison against a Residual-Based Receiver Autonomous Integrity Monitoring (RBRAIM) scheme using simulated and real flight data to evaluate each systems performance.

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Obsolescence: Evaluating An Educational Serious Game On Artificial Intelligence Impacts To Military Strategic Goals, Timothy C. Kokotajlo Mar 2022

Obsolescence: Evaluating An Educational Serious Game On Artificial Intelligence Impacts To Military Strategic Goals, Timothy C. Kokotajlo

Theses and Dissertations

Artificial Intelligence (AI) threatens to bring significant disruption to all aspects of military operations. This research develops a Serious Game (SG) and assessment methodology to provide education on the mindsets required for engaging with disruptive AI technologies. The game, Obsolescence, teaches strategic-level concepts recommended to the Department of Defense (DoD) from a compilation of reports on the current and future state of AI and warfighting. The methodology for assessing the educational value of Obsolescence addresses common challenges such as subjective reporting, control groups, population sizes, and measuring abstract or high levels of learning. The games proposed educational value is tested …

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Coupled Orbit-Attitude Dynamics And Control Of A Cubesat Equipped With A Robotic Manipulator, Charles M. Carr Mar 2022

Coupled Orbit-Attitude Dynamics And Control Of A Cubesat Equipped With A Robotic Manipulator, Charles M. Carr

Theses and Dissertations

This research investigates the utility and expected performance of a robotic servicing CubeSat. The coupled orbit-attitude dynamics of a 6U CubeSat equipped with a four-link serial manipulator are derived. A proportional-integral-derivative controller is implemented to guide the robot through a series of orbital scenarios, including rendezvous and docking following ejection from a chief spacecraft, repositioning the end effector to a desired location, and tracing a desired path with the end effector. Various techniques involving path planning and inverse differential kinematics are leveraged. Simulation results are presented and performance metrics such as settling time, state errors, control use, and system robustness …

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Leveraging Machine Learning For Large Scale Analysis Of Publicly-Available Data For Gnss Interference Events, David K. Stamper Mar 2022

Leveraging Machine Learning For Large Scale Analysis Of Publicly-Available Data For Gnss Interference Events, David K. Stamper

Theses and Dissertations

This research documents architecture and implementation of an enhanced interference detection and classification analysis system, using both a database and storage solution utilizing machine learning algorithms to detect changes in Carrier-to-Noise strength over multiple GNSS sites. The system uses publicly-available government supported receivers to detect interference, and built using FOSS packaged as a programming library through Python. Two algorithms are discussed in terms of enhancing interference detection using both non-machine learning and machine learning approaches. Two algorithms are also discussed which are used for classification of events. In addition, an approach to Large Scale data analytics is demonstrated via a …

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Automated Aircraft Visual Inspection With Artificial Data Generation Enabled Deep Learning, Nathan J. Gaul Mar 2022

Automated Aircraft Visual Inspection With Artificial Data Generation Enabled Deep Learning, Nathan J. Gaul

Theses and Dissertations

Aircraft visual inspection, which is essential to daily maintenance of an aircraft, is expensive and time-consuming to perform. Augmenting trained maintenance technicians with automated UAVs to collect and analyze images for aircraft inspection is an active research topic and a potential application of CNNs. Training datasets for niche research topics such as aircraft visual inspection are small and challenging to produce, and the manual process of labeling these datasets often produces subjective annotations. Recently, researchers have produced several successful applications of artificially generated datasets with domain randomization for training CNNs for real-world computer vision problems. The research outlined herein builds …

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Application Of Machine Learning Models With Numerical Simulations Of An Experimental Microwave Induced Plasma Gasification Reactor, Owen D. Sedej Mar 2022

Application Of Machine Learning Models With Numerical Simulations Of An Experimental Microwave Induced Plasma Gasification Reactor, Owen D. Sedej

Theses and Dissertations

This thesis aims to contribute to the future development of this technology by providing an in-depth literature review of how this technology physically operates and can be numerically modeled. Additionally, this thesis reviews literature of machine learning models that have been applied to gasification to make accurate predictions regarding the system. Finally, this thesis provides a framework of how to numerically model an experimental plasma gasification reactor in order to inform a variety of machine learning models.

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Double Cone Flow Field Reconstruction Between Mach 4 And 12 Using Machine Learning Techniques, Trevor A. Toros Mar 2022

Double Cone Flow Field Reconstruction Between Mach 4 And 12 Using Machine Learning Techniques, Trevor A. Toros

Theses and Dissertations

No abstract provided.

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90snet:, Seth Richard Mar 2022

90snet:, Seth Richard

Honors Capstones

No abstract provided.

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Robots Need The Ability To Navigate Abusive Interactions, Hideki Garcia Goo, Katie Winkle, Tom Williams, Megan K. Strait Mar 2022

Robots Need The Ability To Navigate Abusive Interactions, Hideki Garcia Goo, Katie Winkle, Tom Williams, Megan K. Strait

Computer Science Faculty Publications

Researchers are seeing more and more cases of abusive disinhibition towards robots in public realms. Because robots embody gendered identities, poor navigation of antisocial dynamics may reinforce or exacerbate gender-based violence. It is essential that robots deployed in social settings be able to recognize and respond to abuse in a way that minimises ethical risk. Enabling this capability requires designers to first understand the risk posed by abuse of robots, and hence how humans perceive robot-directed abuse. To that end, we experimentally investigated reactions to a physically abusive interaction between a human perpetrator and a victimized agent. Given extensions of …

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Understanding Student Perspective Of Undergraduate Cybersecurity Programs And Experiences Across Christian Colleges And Universities, Brandon P. Grech Mar 2022

Understanding Student Perspective Of Undergraduate Cybersecurity Programs And Experiences Across Christian Colleges And Universities, Brandon P. Grech

Doctoral Dissertations and Projects

The number of Christian colleges and universities that are offering cybersecurity four-year degrees is rising. The workforce is in dire need of cybersecurity professionals; however, has anybody asked the new cybersecurity professionals in the workforce how their recent academic experience prepared them for such a global need? Research is well-documented about what industry currently needs in cybersecurity professionals; however, this research focused on asking graduates what students need for the workforce. The purpose of this explorative qualitative study was to gain an understanding of the phenomena of the holistic experience (technical, nontechnical, and whole-person) strengths and shortcomings (if any) recent …

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Exploring And Evaluating The Impact Of Covid-19 On Mobility Changes In Singapore, Aldy Gunawan, Linh Chi Tran, Kar Way Tan, I-Lin Wang Mar 2022

Exploring And Evaluating The Impact Of Covid-19 On Mobility Changes In Singapore, Aldy Gunawan, Linh Chi Tran, Kar Way Tan, I-Lin Wang

Research Collection School Of Computing and Information Systems

This paper analyzes the changes in mobility trends due to the impact of the COVID-19 pandemic in Singapore in the six different sectors: Retail and Recreation, Grocery and Pharmacy, Parks, Transit Stations, Workplaces and Residential. The period of observation is from 15 February 2020 to 18 August 2021. The observed patterns obtained from the descriptive data analysis sheds light on the effectiveness of social distancing measures in Singapore as well as the level of compliance among the country’s residents. Correlation analysis is used to explore the relationship between different sectors during the pandemic period. The results reveal a strong sense …

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Match In My Way: Fine-Grained Bilateral Access Control For Secure Cloud-Fog Computing, Shengmin Xu, Jianting Ning, Yingjiu Li, Yinghui Zhang, Guowen Xu, Xinyi Huang, Robert H. Deng Mar 2022

Match In My Way: Fine-Grained Bilateral Access Control For Secure Cloud-Fog Computing, Shengmin Xu, Jianting Ning, Yingjiu Li, Yinghui Zhang, Guowen Xu, Xinyi Huang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Cloud-fog computing is a novel paradigm to extend the functionality of cloud computing to provide a variety of on demand data services via the edge network. Many cryptographic tools have been introduced to preserve data confidentiality against the untrustworthy network and cloud servers. However, how to efficiently identify and retrieve useful data from a large number of ciphertexts without a costly decryption mechanism remains a challenging problem. In this paper, we introduce a cloud fog-device data sharing system (CFDS) with data confidentiality and data source identification simultaneously based on a new cryptographic primitive named matchmaking attribute-based encryption (MABE) by extending …

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Aspect-Based Api Review Classification: How Far Can Pre-Trained Transformer Model Go?, Chengran Yang, Bowen Xu, Junaed Younus Khan, Gias Uddin, Donggyun Han, Zhou Yang, David Lo Mar 2022

Aspect-Based Api Review Classification: How Far Can Pre-Trained Transformer Model Go?, Chengran Yang, Bowen Xu, Junaed Younus Khan, Gias Uddin, Donggyun Han, Zhou Yang, David Lo

Research Collection School Of Computing and Information Systems

APIs (Application Programming Interfaces) are reusable software libraries and are building blocks for modern rapid software development. Previous research shows that programmers frequently share and search for reviews of APIs on the mainstream software question and answer (Q&A) platforms like Stack Overflow, which motivates researchers to design tasks and approaches related to process API reviews automatically. Among these tasks, classifying API reviews into different aspects (e.g., performance or security), which is called the aspect-based API review classification, is of great importance. The current state-of-the-art (SOTA) solution to this task is based on the traditional machine learning algorithm. Inspired by the …

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Investigations On Cogging Torque Mitigation Techniques Of Transverse Flux Motorfor Direct Drive Low-Speed Spacecraft Applications, Ravichandran Mh, Venkatakirthiga Murali, Haridas Tr Mar 2022

Investigations On Cogging Torque Mitigation Techniques Of Transverse Flux Motorfor Direct Drive Low-Speed Spacecraft Applications, Ravichandran Mh, Venkatakirthiga Murali, Haridas Tr

Turkish Journal of Electrical Engineering and Computer Sciences

The transverse flux motor (TFM) is an ideal choice for direct drive high torque applications owing to its proven higher torque density compared to the radial flux and axial flux motors. TFM motors have several merits to be used for spacecraft applications, considering the everlasting demand of the industry for reduction in power and mass. This paper investigates the various cogging torque mitigation techniques for transverse flux motor to be effectively used as the drive motor for precise position control spacecraft requirement. The paper discusses the basic design variables of surface mounted TFM (SM-TFM) that are to be considered for …

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Cryptographically Strong Random Number Generation Using Integrated Cmosphotodiodes For Low-Cost Microcontroller Based Applications, Baykal Sarioğlu Mar 2022

Cryptographically Strong Random Number Generation Using Integrated Cmosphotodiodes For Low-Cost Microcontroller Based Applications, Baykal Sarioğlu

Turkish Journal of Electrical Engineering and Computer Sciences

In this work, we propose a method to generate random numbers for low-cost, low-power, resource-limited low data-rate microcontrollers using integrated CMOS photodiodes. The proposed method utilizes an integrated CMOS photodiode in the photovoltaic mode as the entropy source. The method is based on serially capturing analog values derived from the integrated CMOS photodiode. The entropy of these values increased by a custom algorithm. The proposed random number generator is devised using an integrated CMOS photodiode manufactured in 180 nm standard CMOS technology. The wide applicably of the random number generator is demonstrated by realizing it on a lowcost Arduino UNO …

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