Computer Sciences Commons^™

Campus Mobile History Application, Drew Adan, Christine Sears

Summer Community of Scholars (RCEU and HCR) Project Proposals

No abstract provided.

Why Sine Membership Functions, Sofia Holguin, Javier Viaña, Kelly Cohen, Anca Ralescu, Vladik Kreinovich

Departmental Technical Reports (CS)

In applications of fuzzy techniques to several practical problems -- in particular, to the problem of predicting passenger flows in the airports -- the most efficient membership function is a sine function; to be precise, a portion of a sine function between the two zeros. In this paper, we provide a theoretical explanation for this empirical success.

Need To Combine Interval And Probabilistic Uncertainty: What Needs To Be Computed, What Can Be Computed, What Can Be Feasibly Computed, And How Physics Can Help, Julio Urenda, Vladik Kreinovich, Olga Kosheleva

Departmental Technical Reports (CS)

In many practical situations, the quantity of interest is difficult to measure directly. In such situations, to estimate this quantity, we measure easier-to-measure quantities which are related to the desired one by a known relation, and we use the results of these measurement to estimate the desired quantity. How accurate is this estimate?

Traditional engineering approach assumes that we know the probability distributions of measurement errors; however, in practice, we often only have partial information about these distributions. In some cases, we only know the upper bounds on the measurement errors; in such cases, the only thing we know about …

Computer-Based Scaffolding In Computer Science Education, Rebecca Trinh, Simone Levy

Summer REU Program

No abstract provided.

Explainabilityaudit: An Automated Evaluation Of Local Explainability In Rooftop Image Classification, Duleep Rathgamage Don, Jonathan Boardman, Sudhashree Sayenju, Ramazan Aygun, Yifan Zhang, Bill Franks, Sereres Johnston, George Lee, Dan Sullivan, Girish Modgil

Published and Grey Literature from PhD Candidates

Explainable Artificial Intelligence (XAI) is a key concept in building trustworthy machine learning models. Local explainability methods seek to provide explanations for individual predictions. Usually, humans must check these explanations manually. When large numbers of predictions are being made, this approach does not scale. We address this deficiency for a rooftop classification problem specifically with ExplainabilityAudit, a method that automatically evaluates explanations generated by a local explainability toolkit and identifies rooftop images that require further auditing by a human expert. The proposed method utilizes explanations generated by the Local Interpretable Model-Agnostic Explanations (LIME) framework as the most important superpixels of …

An Attention-Based Resnet Architecture For Acute Hemorrhage Detection And Classification: Toward A Health 4.0 Digital Twin Study, Aftab Hussain, Muhammad Usman Yaseen, Muhammad Imran, Muhammad Waqar, Adnan Akhunzada, Mohammad Al-Ja'afreh, Abdulmotaleb El Saddik

Computer Vision Faculty Publications

Due to the advancement of digital twin (DT) technology, Health 4.0 applications have become reality and starting to take roots. In this article, we focus on intracranial hemorrhage (ICH) which is a life-threatening emergency that needs immediate diagnosis and treatment. ICH is caused by bleeding inside the skull or brain. Radiologists typically examine computed tomography (CT) scans of the patients to determine the ICH and its subtype. But the manual assessment of the CT scan is a complex and time-consuming task. The existing pre-trained convolutional neural network (CNN) models are state-of-the-art for ICH classification. However, they employ poor feature extraction …

Networks Of Disinformation: The Proliferation Of Hate Speech In Chile And Colombia During The Venezuelan Migration Crisis, Isabelle Valdes, Erika Frydenlund (Mentor)

Computer & Information Science: Research Experiences for Undergraduates in Disinformation Detection and Analytics

No abstract provided.

Fake Review Detection, Michael Husk, Faryaneh Poursardar (Mentor)

Computer & Information Science: Research Experiences for Undergraduates in Disinformation Detection and Analytics

No abstract provided.

Module 1: Introduction To Technology Foresight, Risk Management, And Insurtech, Michael Mcshane, C. Ariel Pinto, Hesamoddin Tahami, Hengameh Fakhravar

Developing Technology Foresight: Case Study of AI in InsurTech

Instructional Module 1 for course, Developing Technology Foresight: Case Study of AI in InsurTech.

Module 2: Case Studies Of Ai And Insurtech, Michael Mcshane, C. Ariel Pinto, Hesamoddin Tahami, Hengameh Fakhravar

Developing Technology Foresight: Case Study of AI in InsurTech

Instructional Module 2 for course, Developing Technology Foresight: Case Study of AI in InsurTech.

Module 3: Technology Foresight And Insurtech, Michael Mcshane, C. Ariel Pinto, Hesamoddin Tahami, Hengameh Fakhravar

Developing Technology Foresight: Case Study of AI in InsurTech

Instructional Module 3 for course, Developing Technology Foresight: Case Study of AI in InsurTech.

An Assessment Of Scientific Claim Verification Frameworks: Final Presentation, Ethan Landers, Jian Wu (Mentor)

Computer & Information Science: Research Experiences for Undergraduates in Disinformation Detection and Analytics

No abstract provided.

Insurtech And Distribution, Michael Mcshane, C. Ariel Pinto, Hesamoddin Tahami, Hengameh Fakhravar

Developing Technology Foresight: Case Study of AI in InsurTech

Questions regarding InsurTech and distribution.

Disinformation About Mental Health On Tiktok, Dani Graber, Anne Perrotti (Mentor)

Computer & Information Science: Research Experiences for Undergraduates in Disinformation Detection and Analytics

No abstract provided.

Protecting Blind Screen-Reader Users From Deceptive Content, Ash Dobrenen, Vikas Ashok (Mentor)

Computer & Information Science: Research Experiences for Undergraduates in Disinformation Detection and Analytics

Visually impaired people who want to use a computer rely on screen readers to independently do this. This research focuses on beginning to build a chrome extension in order to help users more safely navigate the internet using a screen reader. to begin collecting the data, a screen reader was used to help determine items in the website that might take the user somewhere they did not mean to go since the link or image was not sufficiently able to be described by the screen reader. Next, those items were tagged with ’data-attribute=”deceptive”’. After, those data-attributes were extracted and tagged …

Human Interaction With Fake News, Autumn Woodson, Sampath Jayarathna (Mentor)

Computer & Information Science: Research Experiences for Undergraduates in Disinformation Detection and Analytics

No abstract provided.

Point Cloud-Based Mapper For Qcd Analysis, Tareq Alghamdi, Yasir Alanazi, Manal Almaeen, Nobuo Sato, Yaohang Li

The Graduate School Posters

In many scientific applications, Inverse problems are challenging. An inverse problem is the process of inferring unknown parameters from observable ones. In this poster, we present our prototype using Point Cloud-based Variational Autoencoder mapping. Data that connects parameters to detector level events is used to train the proposed model. A point cloud is used to describe a series of events that keeps the permutation invariant property and geometric correlations of the events while being flexible with the number of events in the input. The trained Point Cloud-based Variational Autoencoder functions as an effective inverse function from detector level events to …

Insurtech And Actuarial, Michael Mcshane, C. Ariel Pinto, Hesamoddin Tahami, Hengameh Fakhravar

Developing Technology Foresight: Case Study of AI in InsurTech

Questions regarding InsurTech and actuarial work.

Evaluating A Peer Assisted Learning Programme For Mature Access Foundation Students Undertaking Computer Programming At An Irish University, Nevan Bermingham, Frances Boylan, Barry J. Ryan

Articles

Access Foundation Programmes are a widening-participation initiative designed to encourage engagement in higher education among under-represented groups. This includes socioeconomic and educational disadvantage. Mature students in particular enrolled on these programmes experience greater difficulties making the transition to tertiary education, especially when they opt to study disciplines traditionally considered difficult. Computer programming is perceived as a traditionally difficult subject with lower pass rates and progression rates typically than other subjects.

This paper describes the first of a three-cycle action research study examining the perceived effects of a structured Peer Assisted Learning (PAL) Programme for mature students enrolled on a computer …

Discovering The Traces Of Disinformation On Instagram, Haley Bragg, Michele C. Weigle (Mentor)

Computer & Information Science: Research Experiences for Undergraduates in Disinformation Detection and Analytics

Disinformation, which is fabricated, misleading content spread with the intent to deceive others, is accumulating substantial engagements and reaching a vast audience on Instagram. However, the temporary nature of the platform and the security guidelines that remove malicious content make studying this disinformation a challenge. The only way to access removed content and banned accounts that are no longer on the live web is by searching the web archives. In this study, we set out to quantify the replayability and quality of past captures of Instagram accounts, specifically focusing on a group of of anti-vax content creators known as the …

Introduction To The Course, Michael Mcshane, C. Ariel Pinto, Hesamoddin Tahami, Hengameh Fakhravar

Developing Technology Foresight: Case Study of AI in InsurTech

This PDF document describes the course, Developing Technology Foresight: Case Study of AI in InsurTech, and includes learning outcomes and a course outline.

Insurtech And Underwriting, Michael Mcshane, C. Ariel Pinto, Hesamoddin Tahami, Hengameh Fakhravar

Developing Technology Foresight: Case Study of AI in InsurTech

Questions regarding InsurTech and underwriting work.

Insurtech And Claims, Michael Mcshane, C. Ariel Pinto, Hesamoddin Tahami, Hengameh Fakhravar

Developing Technology Foresight: Case Study of AI in InsurTech

Questions related to InsurTech and claims.

Measuring And Comparing Social Bias In Static And Contextual Word Embeddings, Alan Cueva Mora

Dissertations

Word embeddings have been considered one of the biggest breakthroughs of deep learning for natural language processing. They are learned numerical vector representations of words where similar words have similar representations. Contextual word embeddings are the promising second-generation of word embeddings assigning a representation to a word based on its context. This can result in different representations for the same word depending on the context (e.g. river bank and commercial bank). There is evidence of social bias (human-like implicit biases based on gender, race, and other social constructs) in word embeddings. While detecting bias in static (classical or non-contextual) word …

Nlp@Vcu: Crop Characteristic Extraction Framework, Cora Lewis, Bridget Mcinnes, Getiria Onsongo

Summer REU Program

We developed a crop characteristic extraction framework. Starting from a custom SpaCy named entity recognition model, we added pre-trained word embeddings and a part-of-speech based entity expansion post-processing step. Then, we implemented an evaluation framework that functioned as a 5-fold cross validation wrapper for SpaCy custom training. Preliminary results showed improvement in the extraction framework after these additions.

Cansafe: An Mtd Based Approach For Providing Resiliency Against Dos Attack Within In-Vehicle Networks, Ayan Roy, Sanjay Kumar Madria

Computer Science Faculty Research & Creative Works

Trending towards autonomous transportation systems, modern vehicles are equipped with hundreds of sensors and actuators that increase the intelligence of the vehicles with a higher level of autonomy, as well as facilitate increased communication with entities outside the in-vehicle network. However, increase in a contact point with the outside world has exposed the controller area network (CAN) of a vehicle to remote security vulnerabilities. In particular, an attacker can inject fake high priority messages within the CAN through the contact points, while preventing legitimate messages from controlling the CAN (Denial-of-Service (DoS) attack). In this paper, we propose a Moving Target …

More To Less (M2l): Enhanced Health Recognition In The Wild With Reduced Modality Of Wearable Sensors, Huiyuan Yang, Han Yu, Kusha Sridhar, Thomas Vaessen, Inez Myin-Germeys, Akane Sano

Computer Science Faculty Research & Creative Works

Accurately recognizing health-related conditions from wearable data is crucial for improved healthcare outcomes. To improve the recognition accuracy, various approaches have focused on how to effectively fuse information from multiple sensors. Fusing multiple sensors is a common choice in many applications but may not always be feasible in real-world scenarios. For example, although combining bio signals from multiple sensors (i.e., a chest pad sensor and a wrist wearable sensor) has been proved effective for improved performance, wearing multiple devices might be impractical in the free-living context. To solve the challenges, we propose an effective more to less (M2L) learning framework …

Why People Tend To Overestimate Joint Probabilities, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

It is known that, in general, people overestimate the probabilities of joint events. In this paper, we provide an explanation for this phenomenon -- as explanation based on Laplace Indeterminacy Principle and Maximum Entropy approach.

How To Deal With Conflict Of Interest Situations When Selecting The Best Submission, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In many practical situations when we need to select the best submission -- the best paper, the best candidate, etc. -- there are so few experts that we cannot simply dismiss all the experts who have conflict of interest: we do not want them to judge their own submissions, but we would like to take into account their opinions of all other submissions. How can we take these opinions into account? In this paper, we show that a seemingly reasonable idea can actually lead to bias, and we explain how to take these opinions into account without biasing the final …

What Is A Natural Probability Distribution On The Class Of All Continuous Functions: Maximum Entropy Approach Leads To Wiener Measure, Vladik Kreinovich, Saeid Tizpaz-Niari

Departmental Technical Reports (CS)

While many data processing techniques assume that we know the probability distributions, in practice, we often only have a partial information about these probabilities -- so that several different distributions are consistent with our knowledge. Thus, to apply these data processing techniques, we need to select one of the possible probability distributions. There is a reasonable approach for such selection -- the Maximum Entropy approach. This approach selects a uniform distribution if all we know is that the random variable if located in an interval; it selects a normal distribution if all we know is the mean and the variance. …