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2023

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Full-Text Articles in Computer Sciences

Curriculum Design Of Artificial Intelligence And Sustainability In Secondary School, Jinyi Cai, Mei-Po Kwan, Chunyu Hou, Dong Liu, Yeung Yam Oct 2023

Curriculum Design Of Artificial Intelligence And Sustainability In Secondary School, Jinyi Cai, Mei-Po Kwan, Chunyu Hou, Dong Liu, Yeung Yam

I-GUIDE Forum

Artificial Intelligence is revolutionizing numerous sectors with its transformative power, while at the same time, there is an increasing sense of urgency to address sustainability challenges. Despite the significance of both areas, secondary school curriculums still lack comprehensive integration of AI and sustainability education. This paper presents a curriculum designed to bridge this gap. The curriculum integrates progressive objectives, computational thinking competencies and system thinking components across five modules—awareness, knowledge, interaction, empowerment and ethics—to cater to varying learner levels. System thinking components help students understand sustainability in a holistic manner. Computational thinking competencies aim to cultivate computational thinkers to guide …

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Gaxin2-Xo3 Surface Pyramids Interaction With Formaldehyde: Thermodynamic And Sensing Analysis, Mudar Ahmed Abdulsattar Oct 2023

Gaxin2-Xo3 Surface Pyramids Interaction With Formaldehyde: Thermodynamic And Sensing Analysis, Mudar Ahmed Abdulsattar

Karbala International Journal of Modern Science

GaxIn2-xO3 surface pyramids' electronic structures are investigated using density functional theory, including dispersion corrections. Application of GaxIn2-xO3 surface pyramids as a gas sensor for formaldehyde is also performed and compared with experimental findings. These findings show that the energy gap of these pyramids follows closely with the bulk values. The energy gap increases between the two limits, In2O3 and Ga2O3. Applying GaxIn2-xO3 surface pyramids as a gas sensor uses transition state theory formalism. Thermodynamic quantities such as activation Gibbs energy, enthalpy, and entropy are needed for temperature-dependent calculations. A comparison of sensor response which is proportional to reaction rate as …

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Reducing Uncertainty In Sea-Level Rise Prediction: A Spatial-Variability-Aware Approach, Subhankar Ghosh, Shuai An, Arun Sharma, Jayant Gupta, Shashi Shekhar, Aneesh Subramanian Oct 2023

Reducing Uncertainty In Sea-Level Rise Prediction: A Spatial-Variability-Aware Approach, Subhankar Ghosh, Shuai An, Arun Sharma, Jayant Gupta, Shashi Shekhar, Aneesh Subramanian

I-GUIDE Forum

Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty. This problem is important because sea-level rise affects millions of people in coastal communities and beyond due to climate change's impacts on polar ice sheets and the ocean. This problem is challenging due to spatial variability and unknowns such as possible tipping points (e.g., collapse of Greenland or West Antarctic ice-shelf), climate feedback loops (e.g., clouds, permafrost thawing), future policy decisions, and human actions. Most existing climate modeling approaches use the same set of weights globally, during either regression or …

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Peatmoss: Mining Pre-Trained Models In Open-Source Software, Wenxin Jiang, Jason Jones, Jerin Yasmin, Nicholas Synovic, Rajiv Sashti, Sophie Chen, George K. Thiruvathukal, Yuan Tian, James C. Davis Oct 2023

Peatmoss: Mining Pre-Trained Models In Open-Source Software, Wenxin Jiang, Jason Jones, Jerin Yasmin, Nicholas Synovic, Rajiv Sashti, Sophie Chen, George K. Thiruvathukal, Yuan Tian, James C. Davis

Computer Science: Faculty Publications and Other Works

Developing and training deep learning models is expensive, so software engineers have begun to reuse pre-trained deep learning models (PTMs) and fine-tune them for downstream tasks. Despite the widespread use of PTMs, we know little about the corresponding software engineering behaviors and challenges. To enable the study of software engineering with PTMs, we present the PeaTMOSS dataset: Pre-Trained Models in Open-Source Software. PeaTMOSS has three parts: a snapshot of (1) 281,638 PTMs, (2) 27,270 open-source software repositories that use PTMs, and (3) a mapping between PTMs and the projects that use them. We challenge PeaTMOSS miners to discover software engineering …

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Chatgpt And Other Generative Ai: A Game Changer For Special Education?, Jennifer J. Lesh Oct 2023

Chatgpt And Other Generative Ai: A Game Changer For Special Education?, Jennifer J. Lesh

Faculty and Staff Publications & Presentations

Keynote session that involved the audience, had them engage in share outs and exploring, as well as provided open-ended thought provoking questions to share with other stakeholders.

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Enhancing Autism Education: Exploring Interactive Videos And Ai Integration For Effective Teaching, Fatima Ahmed Alraeesi Oct 2023

Enhancing Autism Education: Exploring Interactive Videos And Ai Integration For Effective Teaching, Fatima Ahmed Alraeesi

Thesis/ Dissertation Defenses

Teachers should be aware of how to develop effective teaching methods for students with autism. To achieve the best results, teachers need to thoroughly understand autism and the teaching methods and modifications that work best for these students. Adapting classwork & homework and helping students with language difficulties are effective teaching methods. Teachers can use books to get the necessary information and guidance or take training courses. Keeping students organized and focused is one of the needs of children with autism, as well as the use of visual strategies. Some teachers rely on fixed routines in teaching autistic children. Therefore, …

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Broadband Equity, Access, And Deployment In Nevada, Brad Wimmer Oct 2023

Broadband Equity, Access, And Deployment In Nevada, Brad Wimmer

Policy Briefs and Reports

The $45.45 billion Broadband, Equity, Access, and Deployment (BEAD) program’s primary objective is to extend broadband service to all unserved and underserved locations in the U.S. and its territories. Several industry studies predict that the BEAD program can meet its goal of providing universal access to broadband service if eligible entities execute their grant programs well. My review of the BEAD program indicates that policy makers can enhance the likelihood of program success by designing competitive grant programs that give applicants the incentive to undercut the subsidies proposed by their rivals and provide applicants the flexibility to design networks that …

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Vlc-Assisted Safety Message Dissemination In Roadside Infrastructure-Less Iov Systems: Modeling And Analysis, Yuncong Xie, Dongyang Xu, Tiantian Zhang, Keping Yu, Amir Hussain, Mohsen Guizani Oct 2023

Vlc-Assisted Safety Message Dissemination In Roadside Infrastructure-Less Iov Systems: Modeling And Analysis, Yuncong Xie, Dongyang Xu, Tiantian Zhang, Keping Yu, Amir Hussain, Mohsen Guizani

Machine Learning Faculty Publications

Internet-of-vehicles (IoV) is an emerging paradigm with significant potential to improve traffic efficiency and driving safety. Here, we focus on the design of a novel visible light communication (VLC)-assisted scheme to enable driving safety-related IoV services that require ultra-reliable and low-latency communications (URLLC). Specifically, the vehicle-to-vehicle (V2V) communication mode is adopted to satisfy the ultra-low latency requirement of URLLC in roadside infrastructure-less IoV systems. In the outdoor V2V-VLC scenarios, the quality of the received optical signal is degraded by path loss, atmospheric turbulence and additive noise. In addition, the short-packet feature of URLLC introduces inevitable data decoding errors and imperfect …

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Synthesizing Sentience: Integrating Large Language Models And Autonomous Agents For Emulating Human Cognitive Complexity, Jay Ratican, James Hutson, Daniel Plate Oct 2023

Synthesizing Sentience: Integrating Large Language Models And Autonomous Agents For Emulating Human Cognitive Complexity, Jay Ratican, James Hutson, Daniel Plate

Faculty Scholarship

The paper aims to present a novel methodology for emulating the intricacies of human cognitive complexity by ingeniously integrating large language models with autonomous agents. Grounded in the theoretical framework of the modular mind theory-originally espoused by Fodor and later refined by scholars such as Joanna Bryson—the study seeks to venture into the untapped potential of large language models and autonomous agents in mirroring human cognition. Recent advancements in artificial intelligence, exemplified by the inception of autonomous agents like Age in GPT, auto GPT, and baby AGI, underscore the transformative capacities of these technologies in diverse applications. Moreover, empirical studies …

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Evolution Of Glassy Carbon Derived From Pyrolysis Of Furan Resin, Josh Kemppainen, Ivan Gallegos, Aaron Krieg, Jacob R. Gissinger, Kristopher E. Wise, Margaret Kowalik, Julia A. King, S. Gowtham, Adri Van Duin, Gregory Odegard Oct 2023

Evolution Of Glassy Carbon Derived From Pyrolysis Of Furan Resin, Josh Kemppainen, Ivan Gallegos, Aaron Krieg, Jacob R. Gissinger, Kristopher E. Wise, Margaret Kowalik, Julia A. King, S. Gowtham, Adri Van Duin, Gregory Odegard

Michigan Tech Publications

Glassy carbon (GC) material derived from pyrolyzed furan resin was modeled by using reactive molecular dynamics (MD) simulations. The MD polymerization simulation protocols to cure the furan resin precursor material are validated via comparison of the predicted density and Young's modulus with experimental values. The MD pyrolysis simulations protocols to pyrolyze the furan resin precursor is validated by comparison of calculated density, Young's modulus, carbon content, sp carbon content, the in-plane crystallite size, out-of-plane crystallite stacking height, and interplanar crystallite spacing with experimental results from the literature for furan resin derived GC. The modeling methodology established in this work can …

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A Survey Of Eeg And Machine Learning-Based Methods For Neural Rehabilitation, Jaiteg Singh, Farman Ali, Rupali Gill, Babar Shah, Daehan Kwak Oct 2023

A Survey Of Eeg And Machine Learning-Based Methods For Neural Rehabilitation, Jaiteg Singh, Farman Ali, Rupali Gill, Babar Shah, Daehan Kwak

All Works

One approach to therapy and training for the restoration of damaged muscles and motor systems is rehabilitation. EEG-assisted Brain-Computer Interface (BCI) may assist in restoring or enhancing ‘lost motor abilities in the brain. Assisted by brain activity, BCI offers simple-to-use technology aids and robotic prosthetics. This systematic literature review aims to explore the latest developments in BCI and motor control for rehabilitation. Additionally, we have explored typical EEG apparatuses that are available for BCI-driven rehabilitative purposes. Furthermore, a comparison of significant studies in rehabilitation assessment using machine learning techniques has been summarized. The results of this study may influence policymakers’ …

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Assigning Topics To Documents By Successive Projections, Olga Klopp, Maxim Panov, Suzanne Sigalla, Alexandre B. Tsybakov Oct 2023

Assigning Topics To Documents By Successive Projections, Olga Klopp, Maxim Panov, Suzanne Sigalla, Alexandre B. Tsybakov

Machine Learning Faculty Publications

Topic models provide a useful tool to organize and understand the structure of large corpora of text documents, in particular, to discover hidden thematic structure. Clustering documents from big unstructured corpora into topics is an important task in various fields, such as image analysis, e-commerce, social networks and population genetics. Since the number of topics is typically substantially smaller than the size of the corpus and of the dictionary, the methods of topic modeling can lead to a dramatic dimension reduction. We study the problem of estimating the topic-document matrix, which gives the topics distribution for each document in a …

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Why Micro-Funding? Why Small Businesses Are Important? Analysis Based On First Principles, Hein D. Tran, Edwin Tomy George, Vladik Kreinovich Oct 2023

Why Micro-Funding? Why Small Businesses Are Important? Analysis Based On First Principles, Hein D. Tran, Edwin Tomy George, Vladik Kreinovich

Departmental Technical Reports (CS)

On the one hand, in economics, there is a well-known and well-studied economy of scale: when two smaller companies merge, it lowers their costs and thus, makes them more effective and therefore more competitive. At first glance, this advantage of big size would make economy dominated by big companies -- but in reality, small business remain a significant and important economic sector. Similarly, it is well known and well studied that research collaboration enhances researchers' productivity -- but still a significant portion of important results come from individual efforts. In several applications areas, there are area-specific explanations for this seemingly …

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Local-Global Support For Earth Sciences: Economic Analysis, Uyen Hoang Pham, Aaron Velasco, Vladik Kreinovich Oct 2023

Local-Global Support For Earth Sciences: Economic Analysis, Uyen Hoang Pham, Aaron Velasco, Vladik Kreinovich

Departmental Technical Reports (CS)

Most funding for science comes from taxpayers. So, it is very important to be able to convince taxpayers that this funding is potentially beneficial for them. This task is easier in Earth sciences, e.g., in meteorology, where there are clear local benefits. The problem is that while many people support local studies focused on their region, they do not always have a good understanding of the fact that effective local benefits require also studying surrounding areas -- and what should be the optimal balance between local and (more) global studies. In this paper, on a (somewhat) simplified model of the …

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Approximate Stochastic Dominance Revisited, Chon Van Le, Olga Kosheleva, Vladik Kreinovich Oct 2023

Approximate Stochastic Dominance Revisited, Chon Van Le, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

According to decision theory, in general, to recommend the best of possible actions, we need to know, for each possible action, the probabilities of different outcomes, and we also need to know the decision maker's utility function -- that describes his/her preferences. For some pairs of probability distributions, however, we can make such a recommendation without knowing the exact form of the utility function -- e.g., in financial applications, we only need to know that a larger amount is preferable to a smaller one. Such situations, when we can make decisions based only on the information about probabilities, are known …

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Intelligence Versus Inferno: How Artificial Intelligence Can Be Used To Monitor And Manage Wildfires In Europe, Maxwell Feldman Oct 2023

Intelligence Versus Inferno: How Artificial Intelligence Can Be Used To Monitor And Manage Wildfires In Europe, Maxwell Feldman

Independent Study Project (ISP) Collection

Escalating wildfire occurrences in Europe, particularly in the southern Mediterranean region, are presenting significant challenges to socioeconomic, environmental, and ecosystem services. The increasing frequency and severity of these wildfires are straining resources and emphasizing the need for a better understanding of the relationship between suppression capacity and fire behavior in wildfire management. The following research addresses the urgent need for more proactive, knowledge-based, and technologically driven fire management strategies, throughout all four stages of wildfire response – fuel and land management, fire preparedness, fire suppression, and post-fire management. Artificial Intelligence (AI) is becoming increasingly significant in climate change adaptation, especially …

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Catching Elusive Depression Via Facial Micro-Expression Recognition, Xiaohui Chen, Tony Tie (T.) Luo Oct 2023

Catching Elusive Depression Via Facial Micro-Expression Recognition, Xiaohui Chen, Tony Tie (T.) Luo

Computer Science Faculty Research & Creative Works

Depression is a common mental health disorder that can cause consequential symptoms with continuously depressed mood that leads to emotional distress. One category of depression is Concealed Depression, where patients intentionally or unintentionally hide their genuine emotions through exterior optimism, thereby complicating and delaying diagnosis and treatment and leading to unexpected suicides. In this article, we propose to diagnose concealed depression by using facial micro-expressions (FMEs) to detect and recognize underlying true emotions. However, the extremely low intensity and subtle nature of FMEs make their recognition a tough task. We propose a facial landmark-based Region-of-Interest (ROI) approach to address the …

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Affine Image Registration Of Arterial Spin Labeling Mri Using Deep Learning Networks, Zongpai Zhang, Huiyuan Yang, Yanchen Guo, Nicolas R. Bolo, Matcheri Keshavan, Eve Derosa, Adam K. Anderson, David C. Alsop, Lijun Yin, Weiying Dai Oct 2023

Affine Image Registration Of Arterial Spin Labeling Mri Using Deep Learning Networks, Zongpai Zhang, Huiyuan Yang, Yanchen Guo, Nicolas R. Bolo, Matcheri Keshavan, Eve Derosa, Adam K. Anderson, David C. Alsop, Lijun Yin, Weiying Dai

Computer Science Faculty Research & Creative Works

Convolutional neural networks (CNN) have demonstrated good accuracy and speed in spatially registering high signal-to-noise ratio (SNR) structural magnetic resonance imaging (sMRI) images. However, some functional magnetic resonance imaging (fMRI) images, e.g., those acquired from arterial spin labeling (ASL) perfusion fMRI, are of intrinsically low SNR and therefore the quality of registering ASL images using CNN is not clear. In this work, we aimed to explore the feasibility of a CNN-based affine registration network (ARN) for registration of low-SNR three-dimensional ASL perfusion image time series and compare its performance with that from the state-of-the-art statistical parametric mapping (SPM) algorithm. The …

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A Novel Method Of Decision Making Based On Plithogenic Contradictions, Nivetha Martin, Florentin Smarandache, Sudha S Oct 2023

A Novel Method Of Decision Making Based On Plithogenic Contradictions, Nivetha Martin, Florentin Smarandache, Sudha S

Neutrosophic Systems with Applications

Plithogenic decision-making models are evolved integrating the Plithogenic modelling approach with various methods of multi-criteria decision-making (MCDM). The earlier Plithogenic based decision methods are primarily based on the degrees of appurtenance. This paper introduces a novel Plithogenic ranking genre of decision-making paradigm based on degrees of contradiction. The method of Decision Making on Plithogenic Contradictions (DMPC) developed in this research work is indigenous and unique as the modeling procedure doesn’t resemble any of the decision methods. This simple and logical approach proposed in this paper is applied in making optimal decisions on supplier selection. The proposed contradiction based Plithogenic model …

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Neutrosophic Bicubic B-Spline Surface Interpolation Model For Uncertainty Data, Siti Nur Idara Rosli, Mohammad Izat Emir Zulkifly Oct 2023

Neutrosophic Bicubic B-Spline Surface Interpolation Model For Uncertainty Data, Siti Nur Idara Rosli, Mohammad Izat Emir Zulkifly

Neutrosophic Systems with Applications

Dealing with the uncertainty data problem using neutrosophic data is difficult since certain data are wasted due to noise. To address this issue, this work proposes a neutrosophic set (NS) strategy for interpolating the B-spline surface. The purpose of this study is to visualize the neutrosophic bicubic B-spline surface (NBB-sS) interpolation model. Thus, the principal results of this study introduce the NBB-sS interpolation method for neutrosophic data based on the NS notion. The neutrosophic control net relation (NCNR) is specified first using the NS notion. The B-spline basis function is then coupled to the NCNR to produce the NBB-sS. This …

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Optimal Agricultural Land Use: An Efficient Neutrosophic Linear Programming Method, Maissam Jdid, Florentin Smarandache Oct 2023

Optimal Agricultural Land Use: An Efficient Neutrosophic Linear Programming Method, Maissam Jdid, Florentin Smarandache

Neutrosophic Systems with Applications

The increase in the size of the problems facing humans, their overlap, the division of labor, the multiplicity of departments, as well as the diversity of products and commodities, led to the complexity of business and the emergence of many administrative and production problems. It was necessary to search for appropriate methods to confront these problems. The science of operations research, with its diverse methods, provided the optimal solutions. It addresses many problems and helps in making scientific and thoughtful decisions to carry out the work in the best way within the available capabilities. Operations research is one of the …

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Heart Disease Prediction Under Machine Learning And Association Rules Under Neutrosophic Environment, Ahmed A. El-Douh, Songfeng Lu, Ahmed Abdelhafeez, Ahmed M. Ali, Alber S. Aziz Oct 2023

Heart Disease Prediction Under Machine Learning And Association Rules Under Neutrosophic Environment, Ahmed A. El-Douh, Songfeng Lu, Ahmed Abdelhafeez, Ahmed M. Ali, Alber S. Aziz

Neutrosophic Systems with Applications

Early identification and precise prediction of heart disease have important implications for preventative measures and better patient outcomes since cardiovascular disease is a leading cause of death globally. By analyzing massive amounts of data and seeing patterns that might aid in risk stratification and individualized treatment planning, machine learning algorithms have emerged as valuable tools for heart disease prediction. Predictive modeling is considered for many forms of heart illness, such as coronary artery disease, myocardial infarction, heart failure, arrhythmias, and valvar heart disease. Resource allocation, preventative care planning, workflow optimization, patient involvement, quality improvement, risk-based contracting, and research progress are …

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Transition Supply Chain 4.0 To Supply Chain 5.0: Innovations Of Industry 5.0 Technologies Toward Smart Supply Chain Partners, Mona Mohamed, Karam M. Sallam, Ali Wagdy Mohamed Oct 2023

Transition Supply Chain 4.0 To Supply Chain 5.0: Innovations Of Industry 5.0 Technologies Toward Smart Supply Chain Partners, Mona Mohamed, Karam M. Sallam, Ali Wagdy Mohamed

Neutrosophic Systems with Applications

Industry 4.0 provides businesses with the tools they need to meet difficulties such as fluctuating demand and unstable markets. Additionally, Industry 4.0 refers to the connectivity of computers, various materials, and artificial intelligence (AI) with minimum involvement from humans in the decision-making process. Although Industry 4.0 has a significant potential for the expansion of the industrial sector, it faces several hurdles, including integration of technology, problems with human resources, problems with supply chains, and data security concerns. The human-centered approach that Industry 5.0 took meant that many of the problems that plagued Industry 4.0 could finally be solved. In the …

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Just-In-Accuracy: Mobile Approach To Uncertainty, Martine Ceberio, Christoph Q. Lauter, Vladik Kreinovich Oct 2023

Just-In-Accuracy: Mobile Approach To Uncertainty, Martine Ceberio, Christoph Q. Lauter, Vladik Kreinovich

Departmental Technical Reports (CS)

To make a mobile device last longer, we need to limit computations to a bare minimum. One way to do that, in complex control and decision making problems, is to limit precision with which we do computations, i.e., limit the number of bits in the numbers' representation. A problem is that often, we do not know with what precision should we do computations to get the desired accuracy of the result. What we propose is to first do computations with very low precision, then, based on these computations, estimate what precision is needed to achieve the given accuracy, and then …

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Software Testing And Code Refactoring: A Survey With Practitioners, Danilo Leandro Lima, Ronnie Souza Santos, Guilherme Pires Garcia, Sildemir S. Silva, Cesar Franca, Luiz Fernando Capretz Oct 2023

Software Testing And Code Refactoring: A Survey With Practitioners, Danilo Leandro Lima, Ronnie Souza Santos, Guilherme Pires Garcia, Sildemir S. Silva, Cesar Franca, Luiz Fernando Capretz

Electrical and Computer Engineering Publications

Nowadays, software testing professionals are commonly required to develop coding skills to work on test automation. One essential skill required from those who code is the ability to implement code refactoring, a valued quality aspect of software development; however, software developers usually encounter obstacles in successfully applying this practice. In this scenario, the present study aims to explore how software testing professionals (e.g., software testers, test engineers, test analysts, and software QAs) deal with code refactoring to understand the benefits and limitations of this practice in the context of software testing. We followed the guidelines to conduct surveys in software …

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Metaverse’S Rise And Decline, Nir Kshetri, Jeffrey Voas, Yogesh K. Dwivedi, Diana Rojas Torres, Gayle O'Keefe Oct 2023

Metaverse’S Rise And Decline, Nir Kshetri, Jeffrey Voas, Yogesh K. Dwivedi, Diana Rojas Torres, Gayle O'Keefe

Business: Faculty Publications

Major companies in diverse industries have recently downsized or closed down their metaverse divisions. The authors look at the factors that have led to such strategies.

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How To Deal With Inconsistent Intervals: Utility-Based Approach Can Overcome The Limitations Of The Purely Probability-Based Approach, Kittawit Autchariyapanitkul, Tomoe Entani, Olga Kosheleva, Vladik Kreinovich Oct 2023

How To Deal With Inconsistent Intervals: Utility-Based Approach Can Overcome The Limitations Of The Purely Probability-Based Approach, Kittawit Autchariyapanitkul, Tomoe Entani, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In many application areas, we rely on experts to estimate the numerical values of some quantities. Experts can provide not only the estimates themselves, they can also estimate the accuracies of their estimates -- i.e., in effect, they provide an interval of possible values of the quantity of interest. To get a more accurate estimate, it is reasonable to ask several experts -- and to take the intersection of the resulting intervals. In some cases, however, experts overestimate the accuracy of their estimates, their intervals are too narrow -- so narrow that they are inconsistent: their intersection is empty. In …

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How To Make Machine Learning Financial Recommendations More Fair: Theoretical Explanation, Tho M. Nguyen, Saeid Tizpaz-Niari, Vladik Kreinovich Oct 2023

How To Make Machine Learning Financial Recommendations More Fair: Theoretical Explanation, Tho M. Nguyen, Saeid Tizpaz-Niari, Vladik Kreinovich

Departmental Technical Reports (CS)

Machine learning has been actively and successfully used to make financial decisions. In general, these systems work reasonably well. However, in some cases, these systems show unexpected bias towards minority groups -- the bias that is sometime much larger than the bias in the data on which they were trained. A recent paper analyzed whether a proper selection of hyperparameters can decrease this bias. It turned out that while the selection of hyperparameters indeed affect the system's fairness, only a few of the hyperparameters lead to consistent improvement of fairness: the number of features used for training and the number …

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Csci 632/Csci 492: Machine Learning, David Harrison Oct 2023

Csci 632/Csci 492: Machine Learning, David Harrison

GMAS Course Syllabi

No abstract provided.

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An Ai-Based Framework For Translating American Sign Language To English And Vice Versa, Vijayendra D. Avina, Md Amiruzzaman, Stefanie Amiruzzaman, Linh B. Ngo, M. Ali Akber Dewan Oct 2023

An Ai-Based Framework For Translating American Sign Language To English And Vice Versa, Vijayendra D. Avina, Md Amiruzzaman, Stefanie Amiruzzaman, Linh B. Ngo, M. Ali Akber Dewan

Computer Science Faculty Publications

Abstract: In this paper, we propose a framework to convert American Sign Language (ASL) to English and English to ASL. Within this framework, we use a deep learning model along with the rolling average prediction that captures image frames from videos and classifies the signs from the image frames. The classified frames are then used to construct ASL words and sentences to support people with hearing impairments. We also use the same deep learning model to capture signs from the people with deaf symptoms and convert them into ASL words and English sentences. Based on this framework, we developed a …

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