Computer Sciences Commons^™

Predicting (Economic) Trends: Why Signature Method In Machine Learning, Vladik Kreinovich, Chon Van Le

Departmental Technical Reports (CS)

In many practical situations, we can predict the trend -- i.e., how the system will change -- but we cannot predict the exact timing of this change: this timing may depend on many unpredictable factors. For example, we may be sure that the economy will recover, but how fast it will recover may depend on the status of the pandemic, on the weather-affected agriculture input, etc. In such trend predictions, one of the most efficient methods is signature method, which is based on applying machine learning techniques to several special characteristics of the corresponding time series. In this paper, we …

How To Work? How To Study? Shall We Cram For The Exams? And How Is This Related To Life On Earth?, Olga Kosheleva, Vladik Kreinovich, Nguyen Hoang Phuong

Departmental Technical Reports (CS)

If we follow the same activity for a long time, our productivity decreases. To increase productivity, a natural idea is therefore to switch to a different activity, and then to switch back and resume the current task. On the other hand, after each switch, we need some time to get back to the original productivity. As a result, too frequent switches are also counterproductive. Natural questions are: shall we switch? if yes, when? In this paper, we use a simple model to provide approximate answers to these questions.

Correcting Interval-Valued Expert Estimates: Empirical Formulas Explained, Laura A. Berrout Ramos, Vladik Kreinovich, Kittawit Autchariyapanitkul

Departmental Technical Reports (CS)

Experts' estimates are approximate. To make decisions based on these estimates, we need to know how accurate these estimate are. Sometimes, experts themselves estimate the accuracy of their estimates -- by providing the interval of possible values instead of a single number. In other cases, we can gauge the accuracy of the experts' estimates by asking several experts to estimates the same quantity and using the interval range of these values. In both situations, sometimes the interval is too narrow -- e.g., if an expert is overconfident. Sometimes, the interval is too wide -- if the expert is too cautious. …

Why Moving Fast And Breaking Things Makes Sense?, Francisco Zapata, Eric Smith, Vladik Kreinovich

Departmental Technical Reports (CS)

In the traditional approach to engineering system design, engineers usually come up with several possible designs, each improving on the previous ones. In coming up with these designs, they try their best to make sure that their designs stay within the safety and other constraints, to avoid potential catastrophic crashes. The need for these safety constraints makes this design process reasonably slow. Software engineering at first followed the same pattern, but then realized that since in most cases, failure of a software test does not lead to a catastrophe, it is much faster to first ignore constraints and then adjust …

Why 70/100 Is Satisfactory? Why Five Letter Grades? Why Other Academic Conventions?, Christian Servin, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Why 70/100 is usually a threshold for a student's satisfactory performance? Why there are usually only five letter grades? Why the usual arrangement of research, teaching, and service is 40-40-20? We show that all these arrangements -- and other similar academic arrangements -- can be explained by two ideas: the Laplace Indeterminacy Principle and the seven plus minus two law.

Blessings, God, Sacrifices: Possible Rational Explanations Of Biblical Ideas, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In this paper, we show that many seemingly irrational Biblical ideas can actually be rationally interpreted: that God is everywhere, that we can only say what God is not, that God's name is holy, why cannot you bless as many people as you want, etc. We do not insist on our interpretations, there probably are many others, our sole objective was to show that many Biblical ideas can be rationally explained.

The Affect Of Globalization On Terrorism, Philip R. Passante

Master's Theses

This thesis proposal will dive into the concept of terrorism and how it is an act of force and has proven to be detrimental to the modern world. In addition, this thesis will analyze the concept of terrorism as well as the rationale behind it. It is important to understand and study this as terrorism is a complex entity made up of different themes. The concentration of this thesis will highlight how globalization has affected the phenomena of terrorism in the past, present, and ultimately the future. Globalization and terrorism have a relationship that many scholars and researchers have noticed. …

Multi-Modal Data Fusion, Image Segmentation, And Object Identification Using Unsupervised Machine Learning: Conception, Validation, Applications, And A Basis For Multi-Modal Object Detection And Tracking, Nicholas Lahaye

Computational and Data Sciences (PhD) Dissertations

Remote sensing and instrumentation is constantly improving and increasing in capability. Included within this, is the increase in amount of different instrument types, with various combinations of spatial and spectral resolutions, pointing angles, and various other instrument-specific qualities. While the increase in instruments, and therefore datasets, is a boon for those aiming to study the complexities of the various Earth systems, it can also present a large number of new challenges. With this information in mind, our group has set our aims on combining datasets with different spatial and spectral resolutions in an effective and as-general-as-possible way, with as little …

Mining Bitcoin To Avoid Sanctions, Tyler C. Lubin

Master's Theses

Though the world’s first cryptocurrency, Bitcoin, was introduced over a decade ago, it was not until recently that it became a mainstream subject. While cryptocurrencies offer many advantages, a potential downside for governments, is that no central bank controls the monetary policy and new coins can be mined by anyone anywhere in the world. Governments have always been deeply involved with how a their countries’ currency is ran and the policies they create are meant to keep a currencies’ value stable and make sure other factors like inflation is under control. Even though as of 2021, there were well over …

How To Gauge Students' Ability To Collaborate?, Christian Servin, Olga Kosheleva, Shahnaz Shahbazova, Vladik Kreinovich

Departmental Technical Reports (CS)

Usually, we mostly gauge individual students' skills. However, in the modern world, problems are rarely solved by individuals, it is usually a group effort. So, to make sure that students are successful, we also need to gauge their ability to collaborate. In this paper, we describe when it is possible to gauge the students' ability to collaborate; in situations when such a determination is possible, we explain how exactly we can estimate these abilities.

Representation And Strategy Learning For Variable-Size Tree Transformation Using Reinforcement Learning, Shirin Hosseini Shirvani

Computer Science and Engineering Dissertations - Archive

Trees as acyclic graphs are ubiquitous in representing different context where they encode connectivity patterns at all scales of organization, from biological systems to social networks. Trees are powerful resources which have been used many times for the exploration and discovery of interactions and properties in different context. Tree data structure representation approaches have led to remarkable discoveries in different real-world applications. In the last decades, extensive research and algorithms have been developed on tree or acyclic graph data structures with deep theoretical properties. The cost of solving these various problems ranges from simple linear time algorithms, to more complex …

On The Efficacy Of Knowledge Graph Completion Methods, Accuracy Measures And Evaluation Protocols, Farahnaz Akrami

Computer Science and Engineering Dissertations - Archive

In the active research area of employing embedding models for knowledge graph completion, particularly for the task of link prediction, most prior studies used some specific benchmark datasets to evaluate such models. Most triples in those datasets belong to reverse and duplicate relations, which exhibit high data redundancy due to semantic duplication, correlation, or data incompleteness. This is a case of excessive data leakage—a model is trained using features that otherwise would not be available when the model needs to be applied for real prediction. There are also Cartesian product relations for which every triple formed by the Cartesian product …

Decoupling-Based Approach To Centrality Detection In Heterogeneous Multilayer Networks, Kiran Mukunda

Computer Science and Engineering Theses - Archive

Graph analysis is one of the techniques widely used for data analysis. It is used extensively on single graphs. Its ability to capture entities and relationships makes it an attractive data model. Search on graphs, such as finding triangles, cliques, shortest paths, etc., and aggregate analysis, such as communities, substructure, or centrality measures have well-defined algorithms for single graphs. The centrality measure, which is the focus of this thesis, identifies the most important nodes in a graph or network. While there are many centrality measures, the most commonly used ones are degree and betweenness centrality. Algorithms for analyzing these measures …

Machine Learning Methods To Improve Fairness And Prediction Accuracy On Largesocially Relevant Datasets, Bhanu Chaturvedi Jain

Computer Science and Engineering Dissertations - Archive

Machine learning-based decision support systems bring relief to the decision-makers in many domains such as loan application acceptance, dating, hiring, granting parole, insurance coverage, and medical diagnoses. These support systems facilitate processing tremendous amounts of data to decipher the embedded patterns. However,these decisions can also absorb and amplify bias embedded in the data. An increasing number of applications of machine learning-based decision sup-port systems in a growing number of domains has directed the attention of stake-holders to the accuracy, transparency, interpretability, cost effectiveness, and fairness encompassed in the ensuing decisions. In this dissertation, we have focused on fairness and accuracy …

Domain Adaptive Transfer Learning For Visual Classification, Ashiq Imran

Computer Science and Engineering Dissertations - Archive

Deep Neural Networks have made a significant impact on many computer vision applications with large-scale labeled datasets. However, in many applications, it is expensive and time-consuming to gather large-scale labeled data. With the limited availability of labeled data, it is challenging to obtain great performance. Moreover, in many real-world problems, transfer learning has been applied to cope with limited labeled training data. Transfer learning is a machine learning paradigm where pre-trained models on one task can be reused for another task. This dissertation investigates transfer learning and related machine learning techniques such as domain adaptation on visual categorization applications. At …

Modeling Factual Claims With Semantic Frames: Definitions, Datasets, Tools, And Fact-Checking Applications, Fatma Arslan

Computer Science and Engineering Dissertations - Archive

As social media sites have become major channels for the quick dissemination of news, misinformation has become a significant challenge for our society to tackle. Today fact-checking rests primarily on the shoulders of human fact-checkers who laboriously sift through various trustworthy sources, interview subject experts, and check references before reaching a verdict regarding the degree of truthfulness of a factual claim. Compounded with the speed and scale at which misinformation spreads, the demanding process may leave many harmful factual claims unchecked. In the fight to curb the spread of misinformation, researchers from various disciplines have come forward to assist fact-checkers …

Transitioning From Vue 2 To Vue 3, Adele Kanley

Theses/Capstones/Creative Projects

Frontend development is a field that is constantly changing because of the vast amounts of tools that are made available each year. One of the most popular frameworks being utilized to create fluid user experience is the Vue framework. Branching from the well-known Angular.js, Vue.js is an independent open-source project that is making its mark in the user interface community.

Regardless of the popularity of a framework, updates are inevitable to keep up with the innovations required by the IT Field. To ensure that UNO IS&T students are being offered opportunities to learn and develop in the most update to …

Credit Assignment In Multiagent Reinforcement Learning For Large Agent Population, Arambam James Singh

Dissertations and Theses Collection (Open Access)

In the current age, rapid growth in sectors like finance, transportation etc., involve fast digitization of industrial processes. This creates a huge opportunity for next-generation artificial intelligence system with multiple agents operating at scale. Multiagent reinforcement learning (MARL) is the field of study that addresses problems in the multiagent systems. In this thesis, we develop and evaluate novel MARL methodologies that address the challenges in large scale multiagent system with cooperative setting. One of the key challenge in cooperative MARL is the problem of credit assignment. Many of the previous approaches to the problem relies on agent's individual trajectory which …

Teaching Students How To Code Qualitative Data: An Experiential Activity Sequence For Training Novice Educational Researchers, Jennifer E. Lineback

University of South Florida (USF) M3 Publishing

Coursework on qualitative research methods is common in many collegiate departments, including psychology, nursing, sociology, and education. Instructors for these courses must identify meaningful activities to support their students’ learning of the domain. This paper presents the components of an experiential activity sequence centered on coding and coding scheme development. Each of the three component activities of this sequence is elaborated, as are the students’ experiences during their participation in the activities. Additionally, the issues concerning coding and coding scheme development that typically emerge from students’ participation in these activities are discussed. Results from implementations of both in-person (face-to-face) and …

Testing Artificial Intelligence-Based Software Systems, Jaganmohan Chandrasekaran

Computer Science and Engineering Dissertations - Archive

Artificial Intelligence (AI)-based software systems are increasingly used in high-stake and safety-critical domains, including recidivism prediction, medical diagnosis, and autonomous driving. There is an urgent need to ensure the reliability and correctness of AI-based systems. At the core of AI-based software systems is a machine learning (ML) model that is used to perform tasks such as classification and prediction. Unlike software programs, where a developer explicitly writes the decision logic, ML models learn the decision logic from a large training dataset. Furthermore, many ML models encode the decision logic in the form of mathematic functions that can be quite abstract …

Optimizing The Demand And Distribution Of Power In Smart Grids, Saifullah Khalid

Computer Science and Engineering Dissertations - Archive

The electricity is generated in bulk power plants and transported to the end-user through the transmission and distribution networks. The process incurs heavy losses adding to the operational costs. Secondly, fossil fuels dominate energy generation and are a major source of greenhouse gases. Thirdly, the power grid is vulnerable to natural disasters. The smart grid addresses these challenges by integrating distributed energy resources (DERs) in the distribution system closer to the load and with greater penetration of renewable energy. Renewable energy is key to cutting carbon emissions due to fossil fuel-based electricity generation and reducing operating costs. It can also …

Nb-Iot: Iot Përmes Sistemeve Celulare Brez-Ngushta, Besart Haziri

Theses and Dissertations

Me rritjen e numrit të pajisjeve IoT në ditët e sotme, kërkesa për t’i mbështetur ato vetëm sa rritet, me rreth 7.6 miliardë pajisje IoT aktive në fund të vitit 2019, si dhe me një pritshmëri të rritjes deri në 24.1 miliardë pajisje IoT në vitin 2030. Me numër kaq të madh të pajisjeve që do të lidhen në Internet atëherë na duhet siguri dhe rrjet i besueshëm që i mbështet të gjitha këto pajisje. Mundësia më e mirë për këtë çështje është NB-IoT (Narrowband–Internet of Things).

NB-IoT mundëson konektimin e qindra-mijëra pajisjeve të vogla (sensorëve) në Internet përmes sistemeve …

The Role Of Trust In Advice Acceptance From Non-Human Actors, Rahul Banerjee

Dissertations and Theses Collection (Open Access)

Advancements in technology are now allowing non-human actors in the form of robot-advisors, driverless cars, medical assistants to perform increasingly complex tasks. While technological change is as old as civilization, these non-human actors can do novel tasks. One such task is that they provide advice which is a credence service (Dulleck, & Kerschbamer, 2006). Using a financial services context this thesis studies the role trust plays in advice acceptance.

Robo-advisors are rapidly replacing human financial advisors as the agent-provider for portfolio investment services. For centuries, it was the banker (human financial advisor) who was responsible for providing his investors with …

Cosy: Counterfactual Syntax For Cross-Lingual Understanding, Sicheng Yu, Hao Zhang, Yulei Niu, Qianru Sun, Jing Jiang

Research Collection School Of Computing and Information Systems

Pre-trained multilingual language models, e.g., multilingual-BERT, are widely used in cross-lingual tasks, yielding the state-of-the-art performance. However, such models suffer from a large performance gap between source and target languages, especially in the zero-shot setting, where the models are fine-tuned only on English but tested on other languages for the same task. We tackle this issue by incorporating language-agnostic information, specifically, universal syntax such as dependency relations and POS tags, into language models, based on the observation that universal syntax is transferable across different languages. Our approach, named COunterfactual SYntax (COSY), includes the design of SYntax-aware networks as well as …

Ava: Adversarial Vignetting Attack Against Visual Recognition, Binyu Tian, Felix Juefei-Xu, Qing Guo, Xiaofei Xie, Xiaohong Li, Yang Liu

Research Collection School Of Computing and Information Systems

Vignetting is an inherent imaging phenomenon within almost all optical systems, showing as a radial intensity darkening toward the corners of an image. Since it is a common effect for photography and usually appears as a slight intensity variation, people usually regard it as a part of a photo and would not even want to post-process it. Due to this natural advantage, in this work, we study the vignetting from a new viewpoint, i.e., adversarial vignetting attack (AVA), which aims to embed intentionally misleading information into the vignetting and produce a natural adversarial example without noise patterns. This example can …

Learning And Exploiting Shaped Reward Models For Large Scale Multiagent Rl, Arambam James Singh, Akshat Kumar, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Many real world systems involve interaction among large number of agents to achieve a common goal, for example, air traffic control. Several model-free RL algorithms have been proposed for such settings. A key limitation is that the empirical reward signal in model-free case is not very effective in addressing the multiagent credit assignment problem, which determines an agent's contribution to the team's success. This results in lower solution quality and high sample complexity. To address this, we contribute (a) an approach to learn a differentiable reward model for both continuous and discrete action setting by exploiting the collective nature of …

Forecasting Interaction Order On Temporal Graphs, Wenwen Xia, Yuchen Li, Jianwei Tian, Shenghong Li

Research Collection School Of Computing and Information Systems

Link prediction is a fundamental task for graph analysis and the topic has been studied extensively for static or dynamic graphs. Essentially, the link prediction is formulated as a binary classification problem about two nodes. However, for temporal graphs, links (or interactions) among node sets appear in sequential orders. And the orders may lead to interesting applications. While a binary link prediction formulation fails to handle such an order-sensitive case. In this paper, we focus on such an interaction order prediction (IOP) problem among a given node set on temporal graphs. For the technical aspect, we develop a graph neural …

Gp3: Gaussian Process Path Planning For Reliable Shortest Path In Transportation Networks, Hongliang Guo, Xuejie Hou, Zhiguang Cao, Jie Zhang

Research Collection School Of Computing and Information Systems

This paper investigates the reliable shortest path (RSP) problem in Gaussian process (GP) regulated transportation networks. Specifically, the RSP problem that we are targeting at is to minimize the (weighted) linear combination of mean and standard deviation of the path's travel time. With the reasonable assumption that the travel times of the underlying transportation network follow a multi-variate Gaussian distribution, we propose a Gaussian process path planning (GP3) algorithm to calculate the a priori optimal path as the RSP solution. With a series of equivalent RSP problem transformations, we are able to reach a polynomial time complexity algorithm with guaranteed …

Logbert: Log Anomaly Detection Via Bert, Haixuan Guo

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

When systems break down, administrators usually check the produced logs to diagnose the failures. Nowadays, systems grow larger and more complicated. It is labor-intensive to manually detect abnormal behaviors in logs. Therefore, it is necessary to develop an automated anomaly detection on system logs. Automated anomaly detection not only identifies malicious patterns promptly but also requires no prior domain knowledge. Many existing log anomaly detection approaches apply natural language models such as Recurrent Neural Network (RNN) to log analysis since both are based on sequential data. The proposed model, LogBERT, a BERT-based neural network, can capture the contextual information in …

Algorithms For Covering Barrier Points By Mobile Sensors With Line Constraint, Princy Jain

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

In this thesis, we develop efficient algorithms for the problem of covering barrier points by mobile sensors. Each sensor is represented by a point in the plane with the same covering range r so that any point within distance r from the sensor can be covered by the sensor. Given a set B of m points (called “barrier points”) and a set S of n points (representing the “sensors”) in the plane, the problem is to move the sensors so that each barrier point is covered by at least one sensor and the maximum movement of all sensors is minimized. …