Computer Sciences Commons^™

Spiking Neural Networks And Their Applications: A Review, Kashu Yamazaki, Viet-Khao Vo-Ho, Darshan Bulsara, Ngan Le

Computer Science and Computer Engineering Faculty Publications and Presentations

The past decade has witnessed the great success of deep neural networks in various domains. However, deep neural networks are very resource-intensive in terms of energy consumption, data requirements, and high computational costs. With the recent increasing need for the autonomy of machines in the real world, e.g., self-driving vehicles, drones, and collaborative robots, exploitation of deep neural networks in those applications has been actively investigated. In those applications, energy and computational efficiencies are especially important because of the need for real-time responses and the limited energy supply. A promising solution to these previously infeasible applications has recently been given …

Why Rectified Power (Repu) Activation Functions Are Efficient In Deep Learning: A Theoretical Explanation, Laxman Bokati, Vladik Kreinovich, Joseph Baca, Natasha Rovelli

Departmental Technical Reports (CS)

At present, the most efficient machine learning techniques is deep learning, with neurons using Rectified Linear (ReLU) activation function s(z) = max(0,z), in many cases, the use of Rectified Power (RePU) activation functions (s(z))^p -- for some p -- leads to better results. In this paper, we explain these results by proving that RePU functions (or their "leaky" versions) are optimal with respect that all reasonable optimality criteria.

Classification Of Twitter Vaping Discourse Using Bertweet: Comparative Deep Learning Study, William Baker, Jason B. Colditz, Page D. Dobbs, Huy Mai, Shyam Visweswaran, Justin Zhan, Brian A. Primack

Computer Science and Computer Engineering Faculty Publications and Presentations

Background:

Twitter provides a valuable platform for the surveillance and monitoring of public health topics; however, manually categorizing large quantities of Twitter data is labor intensive and presents barriers to identify major trends and sentiments. Additionally, while machine and deep learning approaches have been proposed with high accuracy, they require large, annotated data sets. Public pretrained deep learning classification models, such as BERTweet, produce higher-quality models while using smaller annotated training sets.

Objective:

This study aims to derive and evaluate a pretrained deep learning model based on BERTweet that can identify tweets relevant to vaping, tweets (related to vaping) of …

Mgard+: Optimizing Multilevel Methods For Error-Bounded Scientific Data Reduction, Xin Liang, Ben Whitney, Jieyang Chen, Lipeng Wan, Qing Liu, Dingwen Tao, James Kress, David Pugmire, Matthew Wolf, Norbert Podhorszki, Scott Klasky

Computer Science Faculty Research & Creative Works

Nowadays, data reduction is becoming increasingly important in dealing with the large amounts of scientific data. Existing multilevel compression algorithms offer a promising way to manage scientific data at scale but may suffer from relatively low performance and reduction quality. In this paper, we propose MGARD+, a multilevel data reduction and refactoring framework drawing on previous multilevel methods, to achieve high-performance data decomposition and high-quality error-bounded lossy compression. Our contributions are four-fold: 1) We propose to leverage a level-wise coefficient quantization method, which uses different error tolerances to quantize the multilevel coefficients. 2) We propose an adaptive decomposition method which …

Over-Measurement Paradox: Suspension Of Thermonuclear Research Center And Need To Update Standards, Hector Reyes, Saeid Tizpaz-Niari, Vladik Kreinovich

Departmental Technical Reports (CS)

In general, the more measurements we perform, the more information we gain about the system and thus, the more adequate decisions we will be able to make. However, in situations when we perform measurements to check for safety, the situation is sometimes opposite: the more additional measurements we perform beyond what is required, the worse the decisions will be: namely, the higher the chance that a perfectly safe system will be erroneously classified as unsafe and therefore, unnecessary additional features will be added to the system design. This is not just a theoretical possibility: exactly this phenomenon is one of …

Everyone Is Above Average: Is It Possible? Is It Good?, Vladik Kreinovich, Olga Kosheleva

Departmental Technical Reports (CS)

Starting with the 1980s, a popular US satirical radio show described a fictitious town Lake Wobegon where ``all children are above average'' -- parodying the way parents like to talk about their children. This everyone-above-average situation was part of the fiction since, if we interpret the average in the precise mathematical sense, as average over all the town's children, then such a situation is clearly impossible. However, usually, when parents make this claim, they do not mean town-wise average, they mean average over all the kids with whom their child directly interacts. Somewhat surprisingly, it turns out that if we …

Why Shapley Value And Its Variants Are Useful In Machine Learning (And In Other Applications), Laxman Bokati, Olga Kosheleva, Vladik Kreinovich, Nguye Ngoc Thach

Departmental Technical Reports (CS)

Shapley value -- a useful way to allocate gains in cooperative games -- has been very successful in machine learning (and in other applications beyond cooperative games). This success is somewhat puzzling, since the usual derivation of the Shapley value is based on requirements like additivity that are natural in cooperative games and but not ents like additivity and is, thus, applicable in the machine learning case as well.

Fair, Equitable, And Just: A Socio-Technical Approach To Online Safety, Daricia Wilkinson

All Dissertations

Socio-technical systems have been revolutionary in reshaping how people maintain relationships, learn about new opportunities, engage in meaningful discourse, and even express grief and frustrations. At the same time, these systems have been central in the proliferation of harmful behaviors online as internet users are confronted with serious and pervasive threats at alarming rates. Although researchers and companies have attempted to develop tools to mitigate threats, the perception of dominant (often Western) frameworks as the standard for the implementation of safety mechanisms fails to account for imbalances, inequalities, and injustices in non-Western civilizations like the Caribbean. Therefore, in this dissertation …

Snerf: Stylized Neural Implicit Representations For 3d Scenes, Thu Nguyen-Phuoc, Feng Liu, Lei Xiao

Computer Science Faculty Publications and Presentations

This paper presents a stylized novel view synthesis method. Applying state-of-the-art stylization methods to novel views frame by frame often causes jittering artifacts due to the lack of cross-view consistency. Therefore, this paper investigates 3D scene stylization that provides a strong inductive bias for consistent novel view synthesis. Specifically, we adopt the emerging neural radiance fields (NeRF) as our choice of 3D scene representation for their capability to render high-quality novel views for a variety of scenes. However, as rendering a novel view from a NeRF requires a large number of samples, training a stylized NeRF requires a large amount …

Robust Sensor Design For The Novel Reduced Models Of The Mead-Marcus Sandwich Beam Equation, Ahmet Aydin

Masters Theses & Specialist Projects

Novel space-discretized Finite Differences-based model reductions are proposed for the partial differential equations (PDE) model of a multi-layer Mead-Marcus-type beam with (i) hinged-hinged and (ii) clamped-free boundary conditions. The PDE model describes transverse vibrations for a sandwich beam whose alternating outer elastic layers constrain viscoelastic core layers, which allow transverse shear. The major goal of this project is to design a single boundary sensor, placed at the tip of the beam, to control the overall dynamics on the beam.

For (i), it is first shown that the PDE model is exactly observable by the so-called nonharmonic Fourier series approach. However, …

Zero Trust Architecture: Framework And Case Study, Cody Shepherd

Cyber Operations and Resilience Program Graduate Projects

The world and business are connected and a business does not exist today that does not have potentially thousands of connections to the Internet in addition to the thousands of connections to other various parts of its own infrastructure. That is the nature of the digital world we live in and there is no chance the number of those interconnections will reduce in the future. Protecting from the “outside” world with a perimeter solution might have been enough to reduce risk to an acceptable level in an organization 20 years ago, but today’s threats are sophisticated, persistent, abundant, and can …

Digital Intimacy In Real Time: Live Streaming Gender And Sexuality, Bo Ruberg, Johanna Brewer

Computer Science: Faculty Publications

This article serves as the guest editors’ introduction to the Television and New Media special issue dedicated to gender and sexuality in live streaming. Live streaming is a key part of the contemporary digital media landscape; it sits at the center of wide-reaching shifts in how culture, entertainment, and labor are expressed and experienced online today. Gender and sexuality are crucial elements of live streaming. Across live streaming’s many forms, these elements manifest in myriad ways: from gendered performances to gender-based harassment, from LGBTQ community building to real-time sex work. This special issue models an interdisciplinary approach to studying gender …

Penguin: A Tool For Predicting Pseudouridine Sites In Direct Rna Nanopore Sequencing Data, Doaa Hassan, Daniel Acevedo, Swapna Vidhur Daulatabad, Quoseena Mir, Sarath Chandra Janga

Computer Science Faculty Publications

Pseudouridine is one of the most abundant RNA modifications, occurring when uridines are catalyzed by Pseudouridine synthase proteins. It plays an important role in many biological processes and also has an importance in drug development. Recently, the single-molecule sequencing techniques such as the direct RNA sequencing platform offered by Oxford Nanopore technologies enable direct detection of RNA modifications on the molecule that is being sequenced, but to our knowledge this technology has not been used to identify RNA Pseudouridine sites. To this end, in this paper, we address this limitation by introducing a tool called Penguin that integrates several developed …

Special Section: Reevaluating Markets For Information, Robert John Kauffman, Thomas A. Weber

Research Collection School Of Computing and Information Systems

As the use of information as a productive and tradeable asset becomes more pervasive, its—often unintended—side-effects start showing. This prompts us to systematically recognize these effects and, once identified, to think about how to use them to our advantage or else mitigate them as much as may be economically and/or socially desirable. In this Special Section, we have assembled three research papers, which take a look at different interesting instances of this question, namely in the context of regulating peer-to-peer sharing markets, the substitution of knowledge workers (or their skills) by artificial intelligence (AI), and the difficulties in appropriating rents …

Reflection As An Agile Course Evaluation Tool, Siaw Ling Lo, Pei Hua Cher, Fernando Bello

Research Collection School Of Computing and Information Systems

Reflection is often used as a tool to analyse student learning, be it for internalizing of acquired knowledge or as a form of seeking help through expression of doubts or misconceptions. However, it can be a challenge to extract relevant information from the free-form reflection text. Often times the workload of manually analyzing the reflection text can be a form of deterrence instead of providing insights in the course delivery for instructors, let alone improving the learning experience. In this paper, we review the current usage of reflection and propose an automated reflection framework, together with an end-to-end analysis of …

Enhancing Security Patch Identification By Capturing Structures In Commits, Bozhi Wu, Shangqing Liu, Ruitao Feng, Xiaofei Xie, Jingkai Siow, Shang-Wei Lin

Research Collection School Of Computing and Information Systems

With the rapid increasing number of open source software (OSS), the majority of the software vulnerabilities in the open source components are fixed silently, which leads to the deployed software that integrated them being unable to get a timely update. Hence, it is critical to design a security patch identification system to ensure the security of the utilized software. However, most of the existing works for security patch identification just consider the changed code and the commit message of a commit as a flat sequence of tokens with simple neural networks to learn its semantics, while the structure information is …

Using Constraint Programming And Graph Representation Learning For Generating Interpretable Cloud Security Policies, Mikhail Kazdagli, Mohit Tiwari, Akshat Kumar

Research Collection School Of Computing and Information Systems

Modern software systems rely on mining insights from business sensitive data stored in public clouds. A data breach usually incurs signifcant (monetary) loss for a commercial organization. Conceptually, cloud security heavily relies on Identity Access Management (IAM) policies that IT admins need to properly confgure and periodically update. Security negligence and human errors often lead to misconfguring IAM policies which may open a backdoor for attackers. To address these challenges, frst, we develop a novel framework that encodes generating optimal IAM policies using constraint programming (CP). We identify reducing dormant permissions of cloud users as an optimality criterion, which intuitively …

The Multisided Complexity Of Fairness In Recommender Systems, Nasim Sonboli, Robin Burke, Michael Ekstrand, Rishabh Mehrotra

Computer Science Faculty Publications and Presentations

Recommender systems are poised at the interface between stakeholders: for example, job applicants and employers in the case of recommendations of employment listings, or artists and listeners in the case of music recommendation. In such multisided platforms, recommender systems play a key role in enabling discovery of products and information at large scales. However, as they have become more and more pervasive in society, the equitable distribution of their benefits and harms have been increasingly under scrutiny, as is the case with machine learning generally. While recommender systems can exhibit many of the biases encountered in other machine learning settings, …

A Machine Learning Approach To Forecasting Sep Intensity And Times Based On Cme And Other Solar Activities, Peter John Thomas

Theses and Dissertations

High intensity Solar Energetic Particle (SEP) events pose severe risks for astronauts and critical infrastructure. The ability to accurately forecast the peak intensity and times of these events would enable preparatory measures to mitigate much of this risk. Machine learning approaches have the potential to use characteristics of CMEs and other space weather phenomena to predict SEP intensities and times. However, the severe sparsity of SEP events in current datasets poses a problem to traditional machine learning techniques. In this work, we present a dataset of proton event intensities and times, as well as features for corresponding CMEs and space …

Breast Cancer-Caps: A Breast Cancer Screening System Based On Capsule Network Utilizing The Multiview Breast Thermal Infrared Images, Devanshu Tiwari, Manish Dixit, Kamlesh Gupta

Turkish Journal of Electrical Engineering and Computer Sciences

This paper proposed an accurate and fully automated breast cancer early screening system called the "Breast Cancer-Caps". The capsule network is used in this approach for the cancer detection in breast utilizing the thermal infrared images for the first time. This capsule network is trained with the help of Dynamic as well as Static breast thermal images dataset consisting of left, right, frontal views along with a new multiview thermal images. These multiview breast thermal images are fabricated by concatenating the conventional left, frontal and right view breast thermal images. The other current and popular deep transfer learning models such …

Artifact Development For The Prediction Of Stress Levels On Higher Education Students Using Machine Learning, Valentina Quiroga, Alejandra Hurtado, José Rojas

ICT

Stress is an adaptative reaction of an organism, human or not, to the demands of fitting in an environment (Kav Vedhara, 1996). When stress originates in an educational context, it is common to refer to it as a student and their mechanisms to adapt and cope with the academic demand. All humans experience stress during their lifetime, but when this overwhelmed feeling is prolonged can affect human behaviour and the ability to deal with physical and emotional pressure, having, as a result, a different range of problems. It is important for higher-level educations institutions, such as colleges and universities, to …

Querai – A Smart Quiz Generator, Elton Da Silva, Fernando Aires Da Silva, Kim Jang Womg, Tai Teei Ho

ICT

QUERAI is a website powered by an Artificial Intelligence Question & Answer quiz generator model aiming to enhance students' learning experience and improve teachers' qualitative work by giving them more time to deal with other activities such as assignment correction, general grading, and class preparation.

Computer-Aided Response-To-Intervention For Reading Comprehension Based On Recommender System, Ming-Chi Liu, Wei-Yang Lin, Chia-Ling Tsai

Publications and Research

In 2019, New York State Education Department announced 54.6% of all students in grades 3 to 8 not meeting the standard of reading proficiency. Motivated by the need for a more efficient intervention model, we propose a recommender system to leverage the technology in machine learning to recommend suitable reading materials for effective intervention. The recommendation is based on the student's prior reading comprehension assessments and also assessments of other students at the same grade level using collaborative filtering. No other prior academic or demographic information of students is available. Two main challenges are lack of explicit ratings of reading …

Advancing Human-Agent Teamwork, Vijayanth Tummala

Theses and Dissertations

Progress in the use of Artificial Intelligence (AI) has been made in many different fields. We are now reaching a point in AI development typical AI implementations are not enough: where we would need humans and AI systems to actively collaborate with each other, basing their actions on the actions and capabilities of each other. This collaboration could be in the form of agents assisting humans processing and analyzing information, assisting humans with smaller physical tasks or working with humans as an equal team member – having the same goals and performing the same tasks – to accomplish a goal. …

Asynchronous Messaging In A P2p System: Defending Against A Storage Exhaustion Attack On Kademlia Dht, Maxim Biro

Theses and Dissertations

An instant messaging service designed using a peer to peer distributed network architecture has many appealing properties it gets for free: high scalability, cheap operational cost and no reliance on a third party to provide the service. However, the nature of the distributed network architecture makes implementing some of the instant messaging features rather challenging, asynchronous messaging being one of them. The asynchronous messaging requires that peers store arbitrary data on behalf of other peers for prolonged periods of time, often measured in days, which, if not kept in check, can be easily abused by malicious actors by spamming the …

Process For Designing And Implementing Provably Verifiable Voting Systems, Kholud Alghamdi

Theses and Dissertations

This research aims to explore processes for designing verifiable voting systems in which certain properties can be proven, exemplified with systems applicable to election processes in Saudi Arabia. The electronic government model has become a substantial channel for governments to connect to businesses and citizens, to develop services, and provide general information. E-voting and in particular online voting is one of the important tools that can be used in political and administrative places where information and communications technology devices and tools are utilized to simplify people’s lives and facilitate the election process and decision making. Election processes let a population …

Learning Depth From Images, Zhenyao Wu

Theses and Dissertations

Estimating depth from images has become a very popular task in computer vision which aims to restore the 3D scene from 2D images and identify important geometric knowledge of the scene. Its performance has been significantly improved by convolutional neural networks in recent years, which surpass the traditional methods by a large margin. However, the natural scenes are usually complicated, and hard to build the correspondence between pixels across frames, such as the region containing moving objects, illumination changes, occlusions, and reflections. This research explores rich and comprehensive spatial correspondence across images and designs three new network architectures for depth …

Strategic Signaling For Utility Control In Audit Games, Jianan Chen, Qin Hu, Honglu Jiang

Informatics and Engineering Systems Faculty Publications

As an effective method to protect the daily access to sensitive data against malicious attacks, the audit mechanism has been widely deployed in various practical fields. In order to examine security vulnerabilities and prevent the leakage of sensitive data in a timely manner, the database logging system usually employs an online signaling scheme to issue an alert when suspicious access is detected. Defenders can audit alerts to reduce potential damage. This interaction process between a defender and an attacker can be modeled as an audit game. In previous studies, it was found that sending real-time signals in the audit …

Deep Convolution Neural Networks For Image Classification, Arun D. Kulkarni

Computer Science Faculty Publications and Presentations

Deep learning is a highly active area of research in machine learning community. Deep Convolutional Neural Networks (DCNNs) present a machine learning tool that enables the computer to learn from image samples and extract internal representations or properties underlying grouping or categories of the images. DCNNs have been used successfully for image classification, object recognition, image segmentation, and image retrieval tasks. DCNN models such as Alex Net, VGG Net, and Google Net have been used to classify large dataset having millions of images into thousand classes. In this paper, we present a brief review of DCNNs and results of our …

Self-Guided Learning To Denoise For Robust Recommendation, Yunjun Gao, Yuntao Du, Yujia Hu, Lu Chen, Xinjun Zhu, Ziquan Fang, Baihua Zheng

Research Collection School Of Computing and Information Systems

The ubiquity of implicit feedback makes them the default choice to build modern recommender systems. Generally speaking, observed interactions are considered as positive samples, while unobserved interactions are considered as negative ones. However, implicit feedback is inherently noisy because of the ubiquitous presence of noisy-positive and noisy-negative interactions. Recently, some studies have noticed the importance of denoising implicit feedback for recommendations, and enhanced the robustness of recommendation models to some extent. Nonetheless, they typically fail to (1) capture the hard yet clean interactions for learning comprehensive user preference, and (2) provide a universal denoising solution that can be applied to …