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2022

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Full-Text Articles in Computer Sciences

How To Make Inflation Optimal And Fair, Sean Aguilar, Vladik Kreinovich Aug 2022

How To Make Inflation Optimal And Fair, Sean Aguilar, Vladik Kreinovich

Departmental Technical Reports (CS)

A reasonably small inflation helps economy as a whole -- by encouraging spending, but it also hurts people by decreasing the value of their savings. It is therefore reasonably to come up with an optimal (and fair) level of inflation, that would stimulate economy without hurting people too much. In this paper, we describe how this can be potentially done.

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Semi Automatic Hand Pose Annotation Using A Single Depth Camera, Marnim Galib Aug 2022

Semi Automatic Hand Pose Annotation Using A Single Depth Camera, Marnim Galib

Computer Science and Engineering Dissertations - Archive

This thesis investigates the problem of 3D hand pose annotation using a single depth camera. While hand pose annotations are critically important for training deep neural networks, creating such reliable training data is challenging and manual labor intensive. Current datasets that rely on manual annotation on real images are limited in size due to the difficulty of annotating them. Although, large datasets have been generated using tracking based methods followed by manual refinement, these methods are prone to annotation errors due to tracking failure. Synthetic images have also been used to create large datasets but synthetic frames does not capture …

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Quantum Algorithms For Attacking Hardness Assumptions In Classical And Post‐Quantum Cryptography, J.-F. Biasse, X. Bonnetain, E. Kirshanova, A. Schrottenloher, Fang Song Aug 2022

Quantum Algorithms For Attacking Hardness Assumptions In Classical And Post‐Quantum Cryptography, J.-F. Biasse, X. Bonnetain, E. Kirshanova, A. Schrottenloher, Fang Song

Computer Science Faculty Publications and Presentations

In this survey, the authors review the main quantum algorithms for solving the computational problems that serve as hardness assumptions for cryptosystem. To this end, the authors consider both the currently most widely used classically secure cryptosystems, and the most promising candidates for post-quantum secure cryptosystems. The authors provide details on the cost of the quantum algorithms presented in this survey. The authors furthermore discuss ongoing research directions that can impact quantum cryptanalysis in the future.

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Effective Sequence Models And Graph Neural Networks For Molecular Data Analysis, Chaochao Yan Aug 2022

Effective Sequence Models And Graph Neural Networks For Molecular Data Analysis, Chaochao Yan

Computer Science and Engineering Dissertations - Archive

Drug discovery is the process of discovering new candidate medications. New drugs are continually developed by pharmaceutical industries to address increasing medical needs. Drug discovery involves a series of processes including target identification and validation, hit identification, lead generation and optimization, and finally the identification of a candidate for further development. The development further includes optimization of chemical synthesis and its formulation, toxicological studies in animals, clinical trials, and eventually regulatory approval. Both of these processes are time-consuming and cost-expensive. Computer-aided drug discovery mainly relies on modern computers to model drug molecules, which can speed up the process of drug …

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A Real-Time Activity Recognition In A Congested Wireless Environment, Israel Oludayo Elujide Aug 2022

A Real-Time Activity Recognition In A Congested Wireless Environment, Israel Oludayo Elujide

Computer Science and Engineering Dissertations - Archive

ABSTRACT: This dissertation reports on how to achieve real-time activity recognition in a congested wireless environment. Recently, human activity recognition with WiFi has been the focus of many researchers due to the limitations of legacy approaches like video cameras and sensors. Users have concerns with privacy when it comes to video activity recognition. Likewise, activity recognition sensors can be expensive, obtrusive, and inconvenient to be worn for an extended period of time. The urgency for implementing contactless activity recognition has also been accelerated due to changes in society and social interaction because of the COVID-19 pandemic. Many public facilities like …

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Hand Analysis From Depth Images, Mohammad Rezaei Aug 2022

Hand Analysis From Depth Images, Mohammad Rezaei

Computer Science and Engineering Dissertations - Archive

Hand analysis using vision systems is necessary for interaction between people and digital devices and thus is crucial in many applications relating to computer vision and human computer interaction (HCI). The proposed dissertation will explore hand analysis from depth images along two lines: hand part segmentation and 3D hand pose estimation. First, we investigate hand part segmentation from depth images, which is formulated as a semantic segmentation task. We explore a method aimed at determining for every pixel what hand part it belongs to. This method attempts to perform this task without requiring the ground-truth segmentation labels for training. It …

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Towards High Performance Cancer Staging From Histology Images, Ashwin Raju Aug 2022

Towards High Performance Cancer Staging From Histology Images, Ashwin Raju

Computer Science and Engineering Dissertations - Archive

Digital Pathology (DP) has been recently used in replacement to traditional microscopy samples as it easy to navigate and can be analysed, processed and saved. With the invention of Digital pathology, there has been exponential increase of automated process to make the life of Doctors easier. One such automated process is Artificial Intelligence (AI) where the AI is used as an assistant to Humans and to make the analysis and guide the experts. With the advent of AI and in particular Deep Learning, research has been divided and focused to solve multiple problems in Digital Pathology. One such important application …

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Gan-Based Domain Translation For Hand Pose Estimation And Face Reconstruction, Farnaz Farahanipad Aug 2022

Gan-Based Domain Translation For Hand Pose Estimation And Face Reconstruction, Farnaz Farahanipad

Computer Science and Engineering Dissertations - Archive

Deep learning solutions for hand pose estimation are now very reliant on comprehensive datasets covering diverse camera perspectives, lighting conditions, shapes, and pose variations. Since, acquiring such datasets is a challenging task that may be infeasible for many novel applications, several studies aim to develop semi/self supervised learning methods, that learn to estimate hand pose from a few labeled/unlabeled data. Therefore, in this dissertation, we investigate new advances in semi/self supervised learning which will remove the bottleneck of obtaining time-consuming frameby- frame manual annotations through generative adversarial networks (GANs). To handle above mentioned challenges, this thesis makes the following contributions. …

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Dynamic Path Planning For Unmanned Aerial Vehicles Under Deadline And Sector Capacity Constraints, Sudharsan Vaidhun, Zhishan Guo, Jiang Bian, Haoyi Xiong, Sajal K. Das Aug 2022

Dynamic Path Planning For Unmanned Aerial Vehicles Under Deadline And Sector Capacity Constraints, Sudharsan Vaidhun, Zhishan Guo, Jiang Bian, Haoyi Xiong, Sajal K. Das

Computer Science Faculty Research & Creative Works

The US National Airspace System is currently operating at a level close to its maximum potential. The limitation comes from the workload demand on the air traffic controllers. Currently, the air traffic flow management is based on the flight path requests by the airline operators, whereas the minimum separation assurance between flights is handled strategically by air traffic control personnel. In this paper, we propose a scalable framework that allows path planning for a large number of unmanned aerial vehicles (UAVs) taking into account the deadline and weather constraints. Our proposed solution has a polynomial-time computational complexity that is also …

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Deep Learning For Protein Property And Structure Prediction, Yuzhi Guo Aug 2022

Deep Learning For Protein Property And Structure Prediction, Yuzhi Guo

Computer Science and Engineering Dissertations - Archive

I present my work towards solving the fundamental, challenging, and valuable problem for protein property and structure prediction. Specifically, I focus on solving the problem from three critical aspects: (1) designing powerful deep learning networks for specific protein structure property prediction tasks; (2) proposing general methods that enhancing the protein sequence homologous feature, which is an important input feature of relevant tasks; (3) developing a self-supervised pre-training model for learning structure embeddings from protein tertiary structures. To evaluate the effectiveness of the developed methods, I apply several protein downstream tasks including protein secondary structure, solvent accessibility, backbone dihedral angles, protein …

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Towards Security Aware Crowdsourcing, Mingyan Xiao Aug 2022

Towards Security Aware Crowdsourcing, Mingyan Xiao

Computer Science and Engineering Dissertations - Archive

Crowdsourcing has emerged as a novel problem-solving paradigm, which facilitates addressing problems by outsourcing them to the crowd. The openness of crowdsourcing renders it vulnerable to misbehaving workers that impair data trustworthiness. They may attempt to submit calibrated data/parameters to manipulate crowdsourcing outcomes for higher beneficial gain. Those misbehaviors would infringe crowdsourcing's process and, overall, its usefulness. In this dissertation, I intend to secure the crowdsourcing platform from worker's untrustworthy data reporting. The main contributions are mainly threefold. First, we secure task allocation, an essential but vulnerable stage in crowdsourcing, from individual misreporting. To be specific, misbehaving workers may manipulate …

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Cyber Deception For Critical Infrastructure Resiliency, Md Ali Reza Al Amin Aug 2022

Cyber Deception For Critical Infrastructure Resiliency, Md Ali Reza Al Amin

Computational Modeling & Simulation Engineering Theses & Dissertations

The high connectivity of modern cyber networks and devices has brought many improvements to the functionality and efficiency of networked systems. Unfortunately, these benefits have come with many new entry points for attackers, making systems much more vulnerable to intrusions. Thus, it is critically important to protect cyber infrastructure against cyber attacks. The static nature of cyber infrastructure leads to adversaries performing reconnaissance activities and identifying potential threats. Threats related to software vulnerabilities can be mitigated upon discovering a vulnerability and-, developing and releasing a patch to remove the vulnerability. Unfortunately, the period between discovering a vulnerability and applying a …

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Evaluation Of Generative Models For Predicting Microstructure Geometries In Laser Powder Bed Fusion Additive Manufacturing, Andy Ramlatchan Aug 2022

Evaluation Of Generative Models For Predicting Microstructure Geometries In Laser Powder Bed Fusion Additive Manufacturing, Andy Ramlatchan

Computer Science Theses & Dissertations

In-situ process monitoring for metals additive manufacturing is paramount to the successful build of an object for application in extreme or high stress environments. In selective laser melting additive manufacturing, the process by which a laser melts metal powder during the build will dictate the internal microstructure of that object once the metal cools and solidifies. The difficulty lies in that obtaining enough variety of data to quantify the internal microstructures for the evaluation of its physical properties is problematic, as the laser passes at high speeds over powder grains at a micrometer scale. Imaging the process in-situ is complex …

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Applied Deep Learning: Case Studies In Computer Vision And Natural Language Processing, Md Reshad Ul Hoque Aug 2022

Applied Deep Learning: Case Studies In Computer Vision And Natural Language Processing, Md Reshad Ul Hoque

Electrical & Computer Engineering Theses & Dissertations

Deep learning has proved to be successful for many computer vision and natural language processing applications. In this dissertation, three studies have been conducted to show the efficacy of deep learning models for computer vision and natural language processing. In the first study, an efficient deep learning model was proposed for seagrass scar detection in multispectral images which produced robust, accurate scars mappings. In the second study, an arithmetic deep learning model was developed to fuse multi-spectral images collected at different times with different resolutions to generate high-resolution images for downstream tasks including change detection, object detection, and land cover …

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Using Ensemble Learning Techniques To Solve The Blind Drift Calibration Problem, Devin Scott Drake Aug 2022

Using Ensemble Learning Techniques To Solve The Blind Drift Calibration Problem, Devin Scott Drake

Computer Science Theses & Dissertations

Large sets of sensors deployed in nearly every practical environment are prone to drifting out of calibration. This drift can be sensor-based, with one or several sensors falling out of calibration, or system-wide, with changes to the physical system causing sensor-reading issues. Recalibrating sensors in either case can be both time and cost prohibitive. Ideally, some technique could be employed between the sensors and the final reading that recovers the drift-free sensor readings. This paper covers the employment of two ensemble learning techniques — stacking and bootstrap aggregation (or bagging) — to recover drift-free sensor readings from a suite of …

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Emotion Detection Using An Ensemble Model Trained With Physiological Signals And Inferred Arousal-Valence States, Matthew Nathanael Gray Aug 2022

Emotion Detection Using An Ensemble Model Trained With Physiological Signals And Inferred Arousal-Valence States, Matthew Nathanael Gray

Electrical & Computer Engineering Theses & Dissertations

Affective computing is an exciting and transformative field that is gaining in popularity among psychologists, statisticians, and computer scientists. The ability of a machine to infer human emotion and mood, i.e. affective states, has the potential to greatly improve human-machine interaction in our increasingly digital world. In this work, an ensemble model methodology for detecting human emotions across multiple subjects is outlined. The Continuously Annotated Signals of Emotion (CASE) dataset, which is a dataset of physiological signals labeled with discrete emotions from video stimuli as well as subject-reported continuous emotions, arousal and valence, from the circumplex model, is used for …

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The Effects Of Side-Channel Attacks On Post-Quantum Cryptography: Influencing Frodokem Key Generation Using The Rowhammer Exploit, Michael Jacob Fahr Aug 2022

The Effects Of Side-Channel Attacks On Post-Quantum Cryptography: Influencing Frodokem Key Generation Using The Rowhammer Exploit, Michael Jacob Fahr

Graduate Theses and Dissertations

Modern cryptographic algorithms such as AES and RSA are effectively used for securing data transmission. However, advancements in quantum computing pose a threat to modern cryptography algorithms due to the potential of solving hard mathematical problems faster than conventional computers. Thus, to prepare for quantum computing, NIST has started a competition to standardize quantum-resistant public-key cryptography algorithms. These algorithms are evaluated for strong theoretical security and run-time performance. NIST is in the third round of the competition, and the focus has shifted to analyzing the vulnerabilities to side-channel attacks. One algorithm that has gained notice is the Round 3 alternate …

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Effective Knowledge Graph Aggregation For Malware-Related Cybersecurity Text, Phillip Ryan Boudreau Aug 2022

Effective Knowledge Graph Aggregation For Malware-Related Cybersecurity Text, Phillip Ryan Boudreau

Graduate Theses and Dissertations

With the rate at which malware spreads in the modern age, it is extremely important that cyber security analysts are able to extract relevant information pertaining to new and active threats in a timely and effective manner. Having to manually read through articles and blog posts on the internet is time consuming and usually involves sifting through much repeated information. Knowledge graphs, a structured representation of relationship information, are an effective way to visually condense information presented in large amounts of unstructured text for human readers. Thusly, they are useful for sifting through the abundance of cyber security information that …

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Scheduling, Complexity, And Solution Methods For Space Robot On-Orbit Servicing, Susan E. Sorenson Aug 2022

Scheduling, Complexity, And Solution Methods For Space Robot On-Orbit Servicing, Susan E. Sorenson

Graduate Theses and Dissertations

This research proposes problems, models, and solutions for the scheduling of space robot on-orbit servicing. We present the Multi-Orbit Routing and Scheduling of Refuellable On-Orbit Servicing Space Robots problem which considers on-orbit servicing across multiple orbits with moving tasks and moving refuelling depots. We formulate a mixed integer linear program model to optimize the routing and scheduling of robot servicers to accomplish on-orbit servicing tasks. We develop and demonstrate flexible algorithms for the creation of the model parameters and associated data sets. Our first algorithm creates the network arcs using orbital mechanics. We have also created a novel way to …

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Secrep : A Framework For Automating The Extraction And Prioritization Of Security Requirements Using Machine Learning And Nlp Techniques, Shada Khanneh Aug 2022

Secrep : A Framework For Automating The Extraction And Prioritization Of Security Requirements Using Machine Learning And Nlp Techniques, Shada Khanneh

Theses, Dissertations and Culminating Projects

Gathering and extracting security requirements adequately requires extensive effort, experience, and time, as large amounts of data need to be analyzed. While many manual and academic approaches have been developed to tackle the discipline of Security Requirements Engineering (SRE), a need still exists for automating the SRE process. This need stems mainly from the difficult, error-prone, and time-consuming nature of traditional and manual frameworks. Machine learning techniques have been widely used to facilitate and automate the extraction of useful information from software requirements documents and artifacts. Such approaches can be utilized to yield beneficial results in automating the process of …

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Secure Retrieval Of Encrypted Similar Documents Using Bloom Filters, German Guzman Aug 2022

Secure Retrieval Of Encrypted Similar Documents Using Bloom Filters, German Guzman

Theses, Dissertations and Culminating Projects

The ability to search for similar documents is a well-known problem on the Web and Information Retrieval field. For example, identifying similar profiles across different government agencies is an important process during intelligence gathering. Nonetheless, when data belongs to multiple parties, internal security policies and government regulations cannot allow the participating parties to freely share their sensitive documents. In our project, we aim to address the following problem: Given a user’s query Q and an encrypted database of documents stored on a third-party cloud server, we want to retrieve top-k documents similar to Q without disclosing Q and the contents …

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Presto : Fast And Effective Group Closeness Maximization, Baibhav L. Rajbhandari Aug 2022

Presto : Fast And Effective Group Closeness Maximization, Baibhav L. Rajbhandari

Legacy Theses & Dissertations (2009 - 2024)

Given a graph and an integer k, the goal of group closeness maximization is to find, among all possible sets of k vertices (called seed sets), a set that has the highest group closeness centrality. Existing techniques for this NP-hard problem strive to quickly find a seed set with a high, but not necessarily the highest centrality.

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Deep Active Genetic Learning With Evidential Uncertainty For Agriculture Crops And Lake Water Quality Assessment, Oguz M. Aranay Aug 2022

Deep Active Genetic Learning With Evidential Uncertainty For Agriculture Crops And Lake Water Quality Assessment, Oguz M. Aranay

Legacy Theses & Dissertations (2009 - 2024)

Despite significant advancements in the field of machine learning, there are two issues that still require further exploration. First, how to learn from a small dataset; and second, how to select appropriate features from the data. Although there exist many techniques to address these issues, choosing a combination of the techniques from these two groups is challenging, and worth investigating. To address these concerns, this thesis presents a learning framework that is based on a deep learning model utilizing active learning (with evidential uncertainty as a basis for acquisition function) for the first issue and a genetic algorithm for the …

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Frameworks For Secure Collaborative And Concurrent Editing, Shashank Arora Aug 2022

Frameworks For Secure Collaborative And Concurrent Editing, Shashank Arora

Legacy Theses & Dissertations (2009 - 2024)

Cloud-based online document editing services, such as Google Docs and Office 365, provide an inexpensive and efficient means of managing documents. However, storing data on the cloud also raises certain security and privacy concerns, especially when the data is of confidential and sensitive nature. Storing data on third-party servers can potentially be compromising as it gives an opportunity to the third-party cloud service providers to turn semi-honest and become curious about user data. User data stored on third-party servers is also prone to attacks like virtual machine-based side-channel along with natural language processing and machine learning-based content search and retrieval …

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Stability And Differential Privacy Of Stochastic Gradient Methods, Zhenhuan Yang Aug 2022

Stability And Differential Privacy Of Stochastic Gradient Methods, Zhenhuan Yang

Legacy Theses & Dissertations (2009 - 2024)

Recently there are a considerable amount of work devoted to the study of the algorithmic stability as well as differential privacy (DP) for stochastic gradient methods (SGM). However, most of the existing work focus on the empirical risk minimization (ERM) and the population risk minimization problems. In this paper, we study two types of optimization problems that enjoy wide applications in modern machine learning, namely the minimax problem and the pairwise learning problem.

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High-Capacity And Interpretable Temporal Point Process Models For User Activity Sequence Modeling, Mengfan Yao Aug 2022

High-Capacity And Interpretable Temporal Point Process Models For User Activity Sequence Modeling, Mengfan Yao

Legacy Theses & Dissertations (2009 - 2024)

A temporal point process can be viewed as a collection of random points falling in the space of time, which is a special type of stochastic processes that is used to model complex event sequences in continuous time.As event data has become more widely available, temporal point process models (TPPs), i.e. techniques for modeling temporal point processes, have been used to solve a wide range of real-world problems, in domains such as e-commerce, online education, and social media. Motivated by the limitations in TPP literature, this dissertation aims to explore and study the following research questions: 1) Focusing on the …

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Fake News Detection On Social Media: A Word Embedding-Based Approach, Muammer Eren Sahin, Chunyang Tang, Mohammad A. Al-Ramahi Aug 2022

Fake News Detection On Social Media: A Word Embedding-Based Approach, Muammer Eren Sahin, Chunyang Tang, Mohammad A. Al-Ramahi

Computer Information Systems Faculty Publications (Archived)

The rapid development of social media, together with the large number of user-generated content on them, has not only connected an unprecedented number of people together to do good stuff, but also has provided convenient platforms to spread misleading pieces of information such as fake news. Existing research has attempted to leverage machine learning to automatically classify fake news. In this paper, we extend such literature by proposing an approach that utilize word embedding and Long Short-Term Memory (LSTM) neural network algorithm. Unlike existing studies, we used two publicly available datasets of news articles to evaluate the proposed model. The …

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Smart Manufacturing—Theories, Methods, And Applications, Zhuming Bi, Lida Xu, Puren Ouyang Aug 2022

Smart Manufacturing—Theories, Methods, And Applications, Zhuming Bi, Lida Xu, Puren Ouyang

Information Technology & Decision Sciences Faculty Publications

(First paragraph) Smart manufacturing (SM) distinguishes itself from other system paradigms by introducing ‘smartness’ as a measure to a manufacturing system; however, researchers in different domains have different expectations of system smartness from their own perspectives. In this Special Issue (SI), SM refers to a system paradigm where digital technologies are deployed to enhance system smartness by (1) empowering physical resources in production, (2) utilizing virtual and dynamic assets over the internet to expand system capabilities, (3) supporting data-driven decision making at all domains and levels of businesses, or (4) reconfiguring systems to adapt changes and uncertainties in dynamic environments. …

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Protein-Protein Interaction Prediction From Language Of Biological Coding, Nayan Howladar Aug 2022

Protein-Protein Interaction Prediction From Language Of Biological Coding, Nayan Howladar

LSU New Orleans Theses and Dissertations

Protein-protein interactions in a cell are essential to the characterization and performance of various fundamental biological processes. Due to the tedious, resource-expensive, and time-consuming experimental processes, computational techniques to solve protein pair interaction difficulties have emerged as an active research area in bioinformatics. This research seeks to develop an innovative machine learning-based technique that predicts the interaction of a protein pair based on carefully selected input features and exploits information-rich evolutionary information. We developed a protein-protein interaction predictor, PPILS, that leverages the evolutionary knowledge from the protein language model. We examined several distinct neural network architectures: CNN+LSTM, Transformer, Encoder-Decoder, and …

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Parallel Algorithms For Scalable Graph Mining: Applications On Big Data And Machine Learning, Naw Safrin Sattar Aug 2022

Parallel Algorithms For Scalable Graph Mining: Applications On Big Data And Machine Learning, Naw Safrin Sattar

LSU New Orleans Theses and Dissertations

Parallel computing plays a crucial role in processing large-scale graph data. Complex network analysis is an exciting area of research for many applications in different scientific domains e.g., sociology, biology, online media, recommendation systems and many more. Graph mining is an area of interest with diverse problems from different domains of our daily life. Due to the advancement of data and computing technologies, graph data is growing at an enormous rate, for example, the number of links in social networks is growing every millisecond. Machine/Deep learning plays a significant role for technological accomplishments to work with big data in modern …

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