Computer Sciences Commons^™

Mask2former With Improved Query For Semantic Segmentation In Remote-Sensing Images, Shichen Guo, Qi Wang, Shiming Xiang, Shuwen Wang, Xuezhi Wang

Computer Science Faculty Publications and Presentations

Semantic segmentation of remote sensing (RS) images is vital in various practical applications, including urban construction planning, natural disaster monitoring, and land resources investigation. However, RS images are captured by airplanes or satellites at high altitudes and long distances, resulting in ground objects of the same category being scattered in various corners of the image. Moreover, objects of different sizes appear simultaneously in RS images. For example, some objects occupy a large area in urban scenes, while others only have small regions. Technically, the above two universal situations pose significant challenges to the segmentation with a high quality for RS …

Mmwave Rat Optimization: Mac Layer Initial Access Design And Transport Layer Integration, Suresh Srinivasan

Dissertations and Theses

MmWave Radio Access Technology (RAT) is a promising technology for wireless communication due its large bandwidth and is already being deployed in 5G cellular and emerging WiFi technologies. MmWave systems use highly directional beams with narrow beamwidths to overcome the high path loss associated with their frequency bands. A mmWave radio can be used either in a standalone mode (where all radios use the same technology) or simultaneously with other technologies such as LTE and low frequency WiFi in a communication mode commonly referred to as integrated mode. This thesis proposes two methods to optimize mmWave RAT performance in both …

Self-Optimizing Feature Generation Via Categorical Hashing Representation And Hierarchical Reinforcement Crossing, Wangyang Ying, Dongjie Wang, Kunpeng Liu, Leilei Sun, Yanjie Fu

Computer Science Faculty Publications and Presentations

Feature generation aims to generate new and meaningful features to create a discriminative representation space. A generated feature is meaningful when the generated feature is from a feature pair with inherent feature interaction. In the real world, experienced data scientists can identify potentially useful feature-feature interactions, and generate meaningful dimensions from an exponentially large search space in an optimal crossing form over an optimal generation path. But, machines have limited human-like abilities. We generalize such learning tasks as self-optimizing feature generation. Self-optimizing feature generation imposes several under-addressed challenges on existing systems: meaningful, robust, and efficient generation. To tackle these challenges, …

Deep Adaptive Graph Clustering Via Von Mises-Fisher Distributions, Pengfei Wang, Daqing Wu, Chong Chen, Kunpeng Liu, Yanjie Fu, Jianqiang Huang, Yuanchun Zhou, Jianfeng Zhan, Xiansheng Hua

Computer Science Faculty Publications and Presentations

Graph clustering has been a hot research topic and is widely used in many fields, such as community detection in social networks. Lots of works combining auto-encoder and graph neural networks have been applied to clustering tasks by utilizing node attributes and graph structure. These works usually assumed the inherent parameters (i.e., size and variance) of different clusters in the latent embedding space are homogeneous, and hence the assigned probability is monotonous over the Euclidean distance between node embeddings and centroids. Unfortunately, this assumption usually does not hold since the size and concentration of different clusters can be quite different, …

Seeing In The Dark: Towards Robust Pedestrian Detection At Nighttime, Afnan Althoupety

Dissertations and Theses

"At some point in the day, everyone is a pedestrian" a message from the National Highway Traffic Safety Administration (NHTSA) about pedestrian safety. In 2020, NHTSA reported that 6,516 pedestrians were killed in traffic crashes and a pedestrian was killed every 81 minutes on average in the United States. In relation to light condition, 77% of pedestrian fatalities occurred in the dark, 20% in daylight, 2% in dusk, and 2% in dawn.

To tackle the issue from a technological perspective, this dissertation addresses the problem of pedestrian detection robustness in dark conditions, benefiting from image processing and learning-based approaches by: …

Parameterized Complexity Of Feature Selection For Categorical Data Clustering, Sayan Bandyapadhyay, Fedor V. Fomin, Petr A. Golovach, Kirill Simonov

Computer Science Faculty Publications and Presentations

We develop new algorithmic methods with provable guarantees for feature selection in regard to categorical data clustering. While feature selection is one of the most common approaches to reduce dimensionality in practice, most of the known feature selection methods are heuristics. We study the following mathematical model. We assume that there are some inadvertent (or undesirable) features of the input data that unnecessarily increase the cost of clustering. Consequently, we want to select a subset of the original features from the data such that there is a small-cost clustering on the selected features. More precisely, for given integers ℓ (the …

Gated Recurrent Units For Blockage Mitigation In Mmwave Wireless, Ahmed H. Almutairi, Alireza Keshavarz-Haddad, Ehsan Aryafar

Computer Science Faculty Publications and Presentations

Millimeter-Wave (mmWave) communication is susceptible to blockages, which can significantly reduce the signal strength at the receiver. Mitigating the negative impacts of blockages is a key requirement to ensure reliable and high throughput mmWave communication links. Previous research on blockage mitigation has introduced several model and protocol based blockage mitigation solutions that focus on one technique at a time, such as handoff to a different base station or beam adaptation to the same base station. In this paper, we address the overarching problem: what blockage mitigation method should be employed? and what is the optimal sub-selection within that method? To …

Preventing Inferences Through Data Dependencies On Sensitive Data, Primal Pappachan, Shufan Zhang, Xi He, Sharad Mehrotra

Computer Science Faculty Publications and Presentations

Simply restricting the computation to non-sensitive part of the data may lead to inferences on sensitive data through data dependencies. Inference control from data dependencies has been studied in the prior work. However, existing solutions either detect and deny queries which may lead to leakage – resulting in poor utility, or only protects against exact reconstruction of the sensitive data – resulting in poor security. In this paper, we present a novel security model called full deniability. Under this stronger security model, any information inferred about sensitive data from non-sensitive data is considered as a leakage. We describe algorithms for …

Energy Auction With Non-Relational Persistence, Michael Ramez Howard

Dissertations and Theses

As the current landscape for electric vehicles changes, options for remote charging are expanding to keep up. In the United States alone, sales of electric vehicles grew 85% from 2020 until hitting 450,000 units by the end of 2021. While these growing sales are encouraging, commercial charging stations have a long way to go before they are as ubiquitous as gasoline stations are today. The peer-to-peer energy auction helps fill the gap in underserved areas by allowing private homeowners to share their charging facilities with other electric vehicle drivers. The auction framework wraps existing charging outlets with a Cloud-connected microcontroller. …

An Overview Of Elements And Relations: Aspects Of A Scientific Metaphysics, Martin Zwick

Complex Systems Faculty Publications and Presentations

A talk on my book, Elements and Relations: Aspects of a Scientific Metaphysics. Book description:

This book develops the core proposition that systems theory is an attempt to construct an “exact and scientific metaphysics,” a system of general ideas central to science that can be expressed mathematically. Collectively, these ideas would constitute a non-reductionist “theory of everything” unlike what is being sought in physics. Inherently transdisciplinary, systems theory offers ideas and methods that are relevant to all of the sciences and also to professional fields such as systems engineering, public policy, business, and social work. To demonstrate the generality …

Effective Entity Augmentation By Querying External Data Sources, Christopher Buss, Jasmin Mousavi, Mikhail Tokarev, Arash Termehchy, David Maier, Stefan Lee

Computer Science Faculty Publications and Presentations

Users often want to augment and enrich entities in their datasets with relevant information from external data sources. As many external sources are accessible only via keyword-search interfaces, a user usually has to manually formulate a keyword query that extract relevant information for each entity. This approach is challenging as many data sources contain numerous tuples, only a small fraction of which may contain entity-relevant information. Furthermore, different datasets may represent the same information in distinct forms and under different terms (e.g., different data source may use different names to refer to the same person). In such cases, it is …

Auxiliary Features-Guided Super Resolution For Monte Carlo Rendering, Qiqi Hou, Feng Liu

Computer Science Faculty Publications and Presentations

This paper investigates super-resolution to reduce the number of pixels to render and thus speed up Monte Carlo rendering algorithms. While great progress has been made to super-resolution technologies, it is essentially an ill-posed problem and cannot recover high-frequency details in renderings. To address this problem, we exploit high-resolution auxiliary features to guide super-resolution of low-resolution renderings. These high-resolution auxiliary features can be quickly rendered by a rendering engine and at the same time provide valuable high-frequency details to assist super-resolution. To this end, we develop a cross-modality transformer network that consists of an auxiliary feature branch and a low-resolution …

Formalizing Stack Safety As A Security Property, Sean Noble Anderson, Roberto Blanco, Leonidas Lampropoulos, Benjamin C. Pierce, Andrew Tolmach

Computer Science Faculty Publications and Presentations

The term stack safety is used to describe a variety of compiler, runtime, and hardware mechanisms for protecting stack memory. Unlike “the heap,” the ISA-level stack does not correspond to a single high-level language concept: different compilers use it in different ways to support procedural and functional abstraction mechanisms from a wide range of languages. This protean nature makes it difficult to nail down what it means to correctly enforce stack safety.

Classification Of Drainage Crossings On High-Resolution Digital Elevation Models: A Deep Learning Approach, Di Wu, Ruopu Li, Banafsheh Rekabdar, Claire Talbert, Michael Edidem, Guangxing Wang

Computer Science Faculty Publications and Presentations

High-Resolution Digital Elevation Models (HRDEMs) have been used to delineate fine-scale hydrographic features in landscapes with relatively level topography. However, artificial flow barriers associated with roads are known to cause incorrect modeled flowlines, because these barriers substantially increase the terrain elevation and often terminate flowlines. A common practice is to breach the elevation of roads near drainage crossing locations, which, however, are often unavailable. Thus, developing a reliable drainage crossing dataset is essential to improve the HRDEMs for hydrographic delineation. The purpose of this research is to develop deep learning models for classifying the images that contain the locations of …

Lossy Kernelization Of Same-Size Clustering, Sayan Bandyapadhyay, Fedor V. Fomin, Petr A. Golovach, Nidhi Purohit, Kirill Simonov

Computer Science Faculty Publications and Presentations

In this work, we study the k-median clustering problem with an additional equal-size constraint on the clusters from the perspective of parameterized preprocessing. Our main result is the first lossy (2-approximate) polynomial kernel for this problem parameterized by the cost of clustering. We complement this result by establishing lower bounds for the problem that eliminate the existence of an (exact) kernel of polynomial size and a PTAS.

The Power Of (Virtual) Convergence: The Unrealized Potential Of Pair Programming And Remote Work, Mikayla Maki

University Honors Theses

Remote work is expensive. It can lead to isolation, miscommunications, and ossified organizations. These problems occur because of a synchronicity mismatch between how we need to communicate as humans, and what today's tools are capable of. This mismatch can be solved by the adoption of remote pair programming, as exemplified by the authors work at a startup (Zed). Pair programming provides the organic, synchronous, reciprocal interaction necessary to develop the sorts of relationships that remote firms currently lack.

Job Management Portal Software Review, Ruchir Elukurthy

University Honors Theses

This essay provides an overview of a computer science capstone project focused on developing a website for Abilities At Work, a non-profit organization. The website aims to assist employment specialists in managing clients' information and tracking their job application in finding meaningful employment. The essay highlights the various stages of the project, understanding requirements, selecting tools and technologies, creating an application architecture, and writing code. Also, this essay focuses on the challenges encountered during the project, along with the valuable lessons learned. This essay emphasizes how the project closely resembles real-world software development, offering insights for prospective students and professionals. …

A Deep Hierarchical Variational Autoencoder For World Models In Complex Reinforcement Learning Environments, Sriharshitha Ayyalasomayajula

Dissertations and Theses

Model-based reinforcement learning (MBRL) approaches leverage learned models of the environment to plan and make optimal decisions, reducing the need for extensive real-world interactions and enabling more efficient learning in complex domains such as robotics, autonomous systems, and resource allocation problems. They also provide interpretability and insight into the underlying dynamics, facilitating better decision-making and system understanding.

The world model is a model-based RL approach that employs generative neural network models to learn a compressed spatial and temporal representation of the environment. This work explores world models and a simple single-layered RNN model to learn a simple policy based on …

How Photorealistic Images Are Generated, Nahom Ketema

University Honors Theses

The field of computer graphics looks into how computers can be used to generate images. From using some trigonometry to plot 3D objects to using rays to calculate the lighting of an object, there are a variety of ways that we can use to draw objects onto a screen. For this thesis, we will be looking at a few of those methods to determine how photorealistic images are generated.

Epl Card Reader Capstone: The Strengths Of Partner Programming From A Team Leader's Perspective, Zach Yost

University Honors Theses

This essay looks to reflect back upon the successes and failures of the EPL Card Reader capstone project, sponsored by Edward Ivory, head of Portland State University's Electronics Prototyping Lab. The EPL Card Reader's goal is to provide a means of tracking and updating student activity and training on the various machines in the lab. Using a local computer port to host this web app a lab administrator or manager is able to scan a student's access badge to review which machines they have been trained on as well as update that training status. The app also has a running …

Implementing A Functional Logic Programming Language Via The Fair Scheme, Andrew Michael Jost

Dissertations and Theses

This document presents a new compiler for the Functional Logic programming language Curry based on a novel pull-tabbing evaluation strategy called the Fair Scheme. A simple version of the Fair Scheme is proven sound, complete, and optimal. An elaborated version is also developed, which supports narrowing computations and other features of Curry, such as constraint programming, equational constraints, and set functions.

The Fair Scheme is used to develop a new Curry system called Sprite, a high-quality, performant implementation whose aims are to promote practical uses of Curry and to serve as a laboratory for further research. An important aspect of …

Quantum Multi-Solution Bernoulli Search With Applications To Bitcoin’S Post-Quantum Security, Alexandru Cojocaru, Juan Garay, Fang Song, Petros Wallden

Computer Science Faculty Publications and Presentations

A proof of work (PoW) is an important cryptographic construct which enables a party to convince other parties that they have invested some effort in solving a computational task. Arguably, its main impact has been in the setting of cryptocurrencies such as Bitcoin and its underlying blockchain protocol, which have received significant attention in recent years due to its potential for various applications as well as for solving fundamental distributed computing questions in novel threat models. PoWs enable the linking of blocks in the blockchain data structure, and thus the problem of interest is the feasibility of obtaining a sequence …

Caspi: Collaborative Photon Processing For Active Single-Photon Imaging, Jongho Lee, Atul Ingle, Jenu V. Chacko, Kevin W. Eliceiri, Mohit Gupta

Computer Science Faculty Publications and Presentations

Image sensors capable of capturing individual photons have made tremendous progress in recent years. However, this technology faces a major limitation. Because they capture scene information at the individual photon level, the raw data is sparse and noisy. Here we propose CASPI: Collaborative Photon Processing for Active Single-Photon Imaging, a technology-agnostic, application-agnostic, and training-free photon processing pipeline for emerging high-resolution single-photon cameras. By collaboratively exploiting both local and non-local correlations in the spatio-temporal photon data cubes, CASPI estimates scene properties reliably even under very challenging lighting conditions. We demonstrate the versatility of CASPI with two applications: LiDAR imaging over a …

Toward Efficient Rendering: A Neural Network Approach, Qiqi Hou

Dissertations and Theses

Physically-based image synthesis has attracted considerable attention due to its wide applications in visual effects, video games, design visualization, and simulation. However, obtaining visually satisfactory renderings with ray tracing algorithms often requires casting a large number of rays and thus takes a vast amount of computation. The extensive computational and memory requirements of ray tracing methods pose a challenge, especially when running these rendering algorithms on resource-constrained platforms, and impede their applications that require high resolutions and refresh rates. This thesis presents three methods to address the challenge of efficient rendering.

First, we present a hybrid rendering method to speed …

Systems Thinking Activities Used In K-12 For Up To Two Decades, Diana Fisher, Systems Thinking Association

Complex Systems Faculty Publications and Presentations

Infusing systems thinking activities in pre-college education (grades K-12) means updating precollege education so it includes a study of many systemic behavior patterns that are ubiquitous in the real world. Systems thinking tools include those using both paper and pencil and the computer and enhance learning in the classroom making it more student-centered, more active, and allowing students to analyze problems that have been heretofore beyond the scope of K-12 classrooms. Students in primary school have used behavior over time graphs to demonstrate dynamics described in story books, like the Lorax, and created stock-flow diagrams to describe what was needed …

Organizational Readiness Assessment For Fraud Detection And Prevention: Case Of Airlines Sector And Electronic Payment, Sultan Ayed Alghamdi

Dissertations and Theses

Payment processing systems have advanced significantly in the airline business. Because e-payments are easy, they have captured the attention of many companies in the aviation industry and are quickly becoming the dominant means of payment. However, as technology advances, fraud grows at a comparable rate. Over the years, there has been a surge in payment fraud incidents in the airline sector, reducing the platform's trustworthiness. Despite attempts to eliminate e-payment fraud, decision-makers lack the technical expertise required to use the finest fraud detection and prevention assessments; this research recognizes the lack of an established decision model as a hurdle and …

An Equivalence Checking Framework For Agile Hardware Design, Yanzhao Wang, Fei Xie, Zhenkun Yang, Pascuale Cocchini, Jin Yang

Computer Science Faculty Publications and Presentations

Agile hardware design enables designers to produce new design iterations efficiently. Equivalence checking is critical in ensuring that a new design iteration conforms to its specification. In this paper, we introduce an equivalence checking framework for hardware designs represented in HalideIR. HalideIR is a popular intermediate representation in software domains such as deep learning and image processing, and it is increasingly utilized in agile hardware design.We have developed a fully automatic equivalence checking workflow seamlessly integrated with HalideIR and several optimizations that leverage the incremental nature of agile hardware design to scale equivalence checking. Evaluations of two deep learning accelerator …

Rdkg: A Reinforcement Learning Framework For Disease Diagnosis On Knowledge Graph, Shipeng Guo, Kunpeng Liu, Pengfei Wang, Weiwei Dai, Yi Du, Yuanchun Zhou, Wenjuan Cui

Computer Science Faculty Publications and Presentations

Automatic disease diagnosis from symptoms has attracted much attention in medical practices. It can assist doctors and medical practitioners in narrowing down disease candidates, reducing testing costs, improving diagnosis efficiency, and more importantly, saving human lives. Existing research has made significant progress in diagnosing disease but was limited by the gap between interpretability and accuracy. To fill this gap, in this paper, we propose a method called Reinforced Disease Diagnosis on Knowlege Graph (RDKG). Specifically, we first construct a knowledge graph containing all information from electronic medical records. To capture informative embeddings, we propose an enhanced knowledge graph embedding method …

The Role Of Preprocessing For Word Representation Learning In Affective Tasks, Nastaran Babanejad, Heidar Davoudi, Ameeta Agrawal, Manos Papagelis

Computer Science Faculty Publications and Presentations

Affective tasks, including sentiment analysis, emotion classification, and sarcasm detection have drawn a lot of attention in recent years due to a broad range of useful applications in various domains. The main goal of affect detection tasks is to recognize states such as mood, sentiment, and emotions from textual data (e.g., news articles or product reviews). Despite the importance of utilizing preprocessing steps in different stages (i.e., word representation learning and building a classification model) of affect detection tasks, this topic has not been studied well. To that end, we explore whether applying various preprocessing methods (stemming, lemmatization, stopword removal, …

An Improved Lower Bound For Sparse Reconstruction From Subsampled Walsh Matrices, Jaroslaw Blasiok, Patrick Lopatto, Kyle Luh, Jake Marcinek, Shravas Rao

Computer Science Faculty Publications and Presentations

We give a short argument that yields a new lower bound on the number of uniformly and independently subsampled rows from a bounded, orthonormal matrix necessary to form a matrix with the restricted isometry property. We show that a matrix formed by uniformly and independently subsampling rows of an N ×N Walsh matrix contains a K-sparse vector in the kernel, unless the number of subsampled rows is Ω(KlogKlog(N/K)) — our lower bound applies whenever min(K,N/K) > logC N. Containing a sparse vector in the kernel precludes not only the restricted isometry property, but more generally the application of those matrices for …