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Articles 1 - 30 of 67
Full-Text Articles in Computer Sciences
Motion Planning Under Uncertainties, Sourav Dutta
Motion Planning Under Uncertainties, Sourav Dutta
Legacy Theses & Dissertations (2009 - 2024)
A robot is an agent that can bring some changes to the environment around it. Motion planning is the problem of carrying out specialized tasks by a robot by either moving itself or some other object (usually called \textit{payload}) from one place to another. In a real-world scenario, a robot is faced with constraints such as momentum, friction, sensor inaccuracies, etc., that can affect its decision-making while performing specialized tasks. These constraints are identified as uncertainties, and successful planning involves making provisions for such uncertainties. In this work, we present methods like stochastic processes, sequential inference, and pattern recognition to …
Probabilistic Forecasting Of Winter Mixed Precipitation Types In New York State Utilizing A Random Forest, Brian Chandler Filipiak
Probabilistic Forecasting Of Winter Mixed Precipitation Types In New York State Utilizing A Random Forest, Brian Chandler Filipiak
Legacy Theses & Dissertations (2009 - 2024)
Operational forecasters face a plethora of challenges when making a forecast; they must consider multiple data sources ranging from radar and satellites to surface and upper air observations, to numerical weather prediction output. Forecasts must be done in a limited window of time, which adds an additional layer of difficulty to the task. These challenges are exacerbated by winter mixed precipitation events where slight differences in thermodynamic profiles or changes in terrain create different precipitation types across small areas. In addition to being difficult to forecast, mixed precipitation events can have large-scale impacts on our society.
Probability Distributions Of The Scalar Potential, Candace Mathews
Probability Distributions Of The Scalar Potential, Candace Mathews
Legacy Theses & Dissertations (2009 - 2024)
In the study of cosmological inflation, string theory and supersymmetry have motivated a wide range of possible inflationary models. These models can be parameterized by a scalar potential V, which is a function of N scalar fields, and determines cosmological parameters such as the vacuum stability and energy density. In principle, we can determine V through high energy physics, such as string theory. In practice, though we may not know the details of V we might have clues about a distribution of plausible V’s, which we can build statistics on to further analyze. The purpose of this thesis defense is …
Turning Density Functional Theory Calculations Into Molecular Mechanics Simulations : Establishing The Fluctuating Density Model For Rna Nucleobases, Christopher A. Myers
Turning Density Functional Theory Calculations Into Molecular Mechanics Simulations : Establishing The Fluctuating Density Model For Rna Nucleobases, Christopher A. Myers
Legacy Theses & Dissertations (2009 - 2024)
Molecular mechanics (MD) simulations and density functional theory (DFT) have been the backbone of computational chemistry for decades. Due to its accuracy and computational feasibility, DFT has become the go-to method for theoretically predicting interaction energies, polarizability, and other electronic properties of small molecules at the quantum mechanical level. Although less fundamental than DFT, molecular mechanics (MM) algorithms have been just as influential in the fields of biology and chemistry, owing their success to the ability to compute measurable, macroscopic quantities for systems with tens of thousands to hundreds of thousands of atoms at a time. Nevertheless, MD simulations would …
Development Of Nucleic Acid Diagnostics For Targeted And Non-Targeted Biosensing, Christopher William Smith
Development Of Nucleic Acid Diagnostics For Targeted And Non-Targeted Biosensing, Christopher William Smith
Legacy Theses & Dissertations (2009 - 2024)
The field of nucleic acid technology is rapidly expanding with new impactful discoveriesbeing made each year. Starting from the discovery of the double-helix structure, cloning, gene editing, polymerase chain reaction (PCR), CRISPR technology, and even the late mRNA vaccines; nucleic acid technology is at the forefront of improving medicine. Nucleic acid technology is extremely versatile due to its easy programmability, automated cheap synthesis, and even its catalog for numerous chemical modifications that can be used to alter structure stability. For example, the number of permutations that can be made with DNA just by altering the code for adenine (A), cytosine …
High-Capacity And Interpretable Temporal Point Process Models For User Activity Sequence Modeling, Mengfan Yao
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 …
Deep Active Genetic Learning With Evidential Uncertainty For Agriculture Crops And Lake Water Quality Assessment, Oguz M. Aranay
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 …
Frameworks For Secure Collaborative And Concurrent Editing, Shashank Arora
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 …
Presto : Fast And Effective Group Closeness Maximization, Baibhav L. Rajbhandari
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.
Stability And Differential Privacy Of Stochastic Gradient Methods, Zhenhuan Yang
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.
Exposing Gan-Generated Faces Using Deep Neural Network, Hui Guo
Exposing Gan-Generated Faces Using Deep Neural Network, Hui Guo
Legacy Theses & Dissertations (2009 - 2024)
Generative adversarial network (GAN) generated high-realistic human faces are visually challenging to discern from real ones. They have been used as profile images for fake social media accounts, which leads to high negative social impacts.In this work, we explore a universal physiological cue of the eye, namely the pupil shape consistency, to identify GAN-generated faces reliably. We show that GAN-generated faces can be exposed via irregular pupil shapes. This phenomenon is caused by the lack of physiological constraints in the GAN models. We demonstrate that such artifacts exist widely in high-quality GAN-generated faces. We design an automatic method to segment …
The Foundations Of Inference And Its Application To Fundamental Physics, Nicholas Matthew Carrara
The Foundations Of Inference And Its Application To Fundamental Physics, Nicholas Matthew Carrara
Legacy Theses & Dissertations (2009 - 2024)
This thesis concerns the foundations of inference – probability theory,entropic inference, information geometry, etc. – and its application to the Entropic Dynamics (ED) approach to Quantum Mechanics (QM) [21, 22, 41, 53, 56–61, 150–153, 165, 195, 196, 268]. The first half of this thesis, chapters 2-6, concern the development of the inference framework. We begin in chapter 2 by discussing de- ductive inference, which involves formal logic and it’s role in access- ing the truth of propositions. We eventually discover that deductive inference is incomplete, in that it can’t address situations in which we have incomplete information. This necessitates a …
Understanding Complex Human Activities In Videos : The Study Of Concurrent Activity Detection And Group Activity Recognition, Yi Wei
Legacy Theses & Dissertations (2009 - 2024)
Human activity understanding, as one of the most important task in video analysis, has been studied for decades. Great efforts have been made to push the activity recognition models towards effective and efficient representation learning. However, it is difficult to define an explicit semantic organization of activities, even for human. Current activity recognition benchmarks only organize the activity labels with shallow hierarchies, which hinders the development of activity recognition system.
Theoretical And Observational Analysis Of Ice Particles For Improvement Of Ice Microphysical Models, Vanessa Przybylo
Theoretical And Observational Analysis Of Ice Particles For Improvement Of Ice Microphysical Models, Vanessa Przybylo
Legacy Theses & Dissertations (2009 - 2024)
Frozen hydrometeors can grow to acquire a multitude of shapes and sizes, which influence the distribution of mass within cloud systems. Aggregates have a variety of formations based on initial ice particle size, shape, falling orientation, and the number of particles that collect. This work employs the theoretical Ice Particle and Aggregate Simulator (IPAS) as a statistical tool to repetitively collect ice crystals to derive bulk aggregate characteristics.
Mining Subgroups From Temporal Data : From The Parts To The Whole, Alexander Gorovits
Mining Subgroups From Temporal Data : From The Parts To The Whole, Alexander Gorovits
Legacy Theses & Dissertations (2009 - 2024)
A variety of dynamic systems can be broken down into potentially overlapping subcomponents with varying temporal behavior, ranging from communities in networks, to clusters of trajectories in spatiotemporal data, to co-evolving subsets within multivariate time series. Using explicit regularization on various temporal behaviors within a tensor factorizationframework, I demonstrate means to mine these subgroups along with their temporal activities, as well as how that yields information about the overall systems. Additionally, I adapt this notion of temporal communities to the spatiotemporal setting to develop a reinforcement learning approach for optimizing co-ordinated communication between independent agents.
Learning Graphs For Object Tracking And Counting, Shengkun Li
Learning Graphs For Object Tracking And Counting, Shengkun Li
Legacy Theses & Dissertations (2009 - 2024)
As important problems in computer vision, object tracking and counting attract increasing amounts of attention in recent years due to its wide range of applications, such as video surveillance, human- computer interaction, smart city. Despite much progress has been made in object tracking and counting with the arriving of deep neural networks (DNN), there still remains much room for improvement to satisfy the real-world applications.
The Role Of Ammonia In Atmospheric New Particle Formation And Implications For Cloud Condensation Nuclei, Arshad Arjunan Nair
The Role Of Ammonia In Atmospheric New Particle Formation And Implications For Cloud Condensation Nuclei, Arshad Arjunan Nair
Legacy Theses & Dissertations (2009 - 2024)
Atmospheric ammonia has received recent attention due to (a) its increasing trend across various regions of the globe; (b) the associated direct and indirect (through PM2.5) effects on human health, the ecosystem, and climate; and (c) recent evidence of its role in significantly enhancing atmospheric new particle formation (NPF or nucleation) rates. The mechanisms behind nucleation in the atmosphere are not fully understood, although over the last decade there have been significant developments in our understanding. This dissertation aims at improving our understanding of atmospheric ammonia in the atmosphere, its spatiotemporal variability, its role in atmospheric new particle formation, and …
Structured Data Mining Networks, Time Series, And Time Series Of Networks, Lin Zhang
Structured Data Mining Networks, Time Series, And Time Series Of Networks, Lin Zhang
Legacy Theses & Dissertations (2009 - 2024)
The rate at which data is generated in modern applications has created an unprecedented demand for novel methods to effectively and efficiently extract insightful patterns. Methods aware of known domain-specific structure in the data tend to be advantageous. In particular, a joint temporal and networked view of observations offers a holistic lens to many real-world systems. Example domains abound: activity of social network users, gene interactions over time, a temporal load of infrastructure networks, and others. Existing analysis and mining approaches for such data exhibit limited quality and scalability due to their sensitivity to noise, missing observations, and the need …
Detecting And Protecting Against Ai-Synthesized Faces, Yuezun Li
Detecting And Protecting Against Ai-Synthesized Faces, Yuezun Li
Legacy Theses & Dissertations (2009 - 2024)
The recent advances in deep learning and the availability of vast volume of online personal images and videos have drastically improved the reality of synthesized faces in images and videos. While there are interesting and creative applications of the AI face synthesis systems, they can also be weaponized, as it can create the illusions of a person's presence and activities that do not occur in reality, which results in serious political, social, financial, and legal consequences. Therefore, it is of great importance to develop effective method to expose the AI-synthesized faces. In this thesis, a set of our recent efforts …
Discriminative Factorization Models For Student Behavioral Pattern Detection And Classification, Mehrdad Mirzaei
Discriminative Factorization Models For Student Behavioral Pattern Detection And Classification, Mehrdad Mirzaei
Legacy Theses & Dissertations (2009 - 2024)
The goal of this dissertation is to examine factors such as how a student chooses to engage with the online platform and time spent on individual tasks and draw conclusions to improve the efficiency of the students and efficacy of online learning tools. Student activities and decision-making while functioning in a computer-based learning environment are utilized to guide students with effective patterns in studying. In addition to the sequence of actions, we have considered the time spent on each activity in modeling to have a more accurate representation of students' behavior in studying. Using sequential pattern mining methods, we find …
Towards Practical Modulation Recognition For Future Spectrum-Sharing Applications, Wei Xiong
Towards Practical Modulation Recognition For Future Spectrum-Sharing Applications, Wei Xiong
Legacy Theses & Dissertations (2009 - 2024)
With recent advances in emerging Dynamic Spectrum Access (DSA) and Cognitive Radio technologies, modulation recognition (ModRec) has emerged as a critical problem with importance to spectrum-sharing applications. Existing approaches, target modulation recognition as if a packet will be decoded in full and thus, pose stringent requirements on spectrum sensing and transmitter behavior: (i) a transmitter's bandwidth should be scanned alone and in full, (ii) for MIMO ModRec, the sensor should have at least same as many antennas as the transmitter, (iii) modulation symbol representation should be uniform and (iv) prior knowledge of the transmitter's technology should be available. These stringent …
Sequentially-Closed And Forward-Closed String Rewriting Systems, Yu Zhang
Sequentially-Closed And Forward-Closed String Rewriting Systems, Yu Zhang
Legacy Theses & Dissertations (2009 - 2024)
In this dissertation we introduce the new concept of sequentially-closed string rewriting systems which generalizes forward-closed string rewriting systems and monadic string rewriting systems. We also investigate subclasses and properties of finite and regular sequentially-closed systems and forward-closed systems.
Uncertainty Learning In Subjective Logic And Pattern Discovery In Network Data, Adilijiang Alimu
Uncertainty Learning In Subjective Logic And Pattern Discovery In Network Data, Adilijiang Alimu
Legacy Theses & Dissertations (2009 - 2024)
Uncertainty caused by unreliable or insufficient data and vulnerable machine learning models
Invariant-Based Online Software Anomaly Detection And Selective Regression Testing, Yizhen Chen
Invariant-Based Online Software Anomaly Detection And Selective Regression Testing, Yizhen Chen
Legacy Theses & Dissertations (2009 - 2024)
Software has been extensively used in various domains to provide online services. With the growing popularity of these types of applications, the quality of the software has a great impact on many of our daily activities [1]. Reliable software executions that deliver expected outcomes are essential for quality services. Software is considered abnormal when its behavior deviates from what is expected at any point during its execution. When anomalous behavior propagates to an exit point of the software and produces an incorrect output or an unexpected termination of the execution, it is considered a software failure. An anomaly may or …
Wrinkles In Time : An Exploration Of Non-Uniform Temporal Resolution In Network Data, Daniel John Ditursi
Wrinkles In Time : An Exploration Of Non-Uniform Temporal Resolution In Network Data, Daniel John Ditursi
Legacy Theses & Dissertations (2009 - 2024)
The continued proliferation of timestamped network data demands increasing sophistication in the analysis of that data. In particular, the literature amply demonstrates that the choice of temporal resolution has a profound impact on the solutions produced by many different methods in this domain -- answers differ when data is viewed second-by-second as opposed to week-by-week. Additionally, research also shows quite clearly that the rates at which network events happen are not constant -- some times are "faster" or "slower" than others, and these variations are not necessarily predictable. Given the above, it is clear that there must be problem settings …
Pose Based Human Activity Recognition, Wenbo Li
Pose Based Human Activity Recognition, Wenbo Li
Legacy Theses & Dissertations (2009 - 2024)
Pose based human activity recognition is an important step towards video understanding. The last decade has witnessed the great progress in this field which is driven by multiple technical innovations, i.e., kinect, pose estimation techniques, deep learning, etc.
Towards The Development Of A Concurrent Programming Language, Marrium Ayesha
Towards The Development Of A Concurrent Programming Language, Marrium Ayesha
Legacy Theses & Dissertations (2009 - 2024)
In order to fully utilize the potential of current architectures, programmers must program withconcurrency in mind. Concurrent processes can be extremely challenging to reason about due tounexpected program behavior that may emerge from interaction between processes. One approachto deal with this difficulty is to study new programming languages that offer an abstraction forconcurrency. This thesis focuses on developing a logical interpretation for concurrent processesand incorporating it in an existing functional programming language called SML. We developthis feature upon the fact that a proof of a theorem in logic can be expressed as a program in aprogramming language. This relation allows …
Efficient Algorithms For Mining Healthcare Data :, Yan Hu
Efficient Algorithms For Mining Healthcare Data :, Yan Hu
Legacy Theses & Dissertations (2009 - 2024)
Data-Driven Healthcare (DDH) is defined as the usage of available medical big data to provide the best and most personalized care, which is believed to be one of the most promising directions for transforming healthcare. The healthcare data includes claims and cost data, clinical data, pharmaceutical R&D data, patient behavior and sentiment data, and health data on the web. There has been a remarkable upsurge in the adoption of healthcare data over the past several years. In particular, it has been used for medical concept extraction, patient trajectory modeling, disease inference, etc.
Efficient Detection Of Diseases By Feature Engineering Approach From Chest Radiograph, Avishek Mukherjee
Efficient Detection Of Diseases By Feature Engineering Approach From Chest Radiograph, Avishek Mukherjee
Legacy Theses & Dissertations (2009 - 2024)
Deep Learning is the new state-of-the-art technology in Image Processing. We applied Deep Learning techniques for identification of diseases from Radiographs made publicly available by NIH. We applied some Feature Engineering approach to augment the data from Anterior-Posterior position to Posterior-Anterior position and vice-versa for all the diseases, at the same point we suppressed ‘No Finding’ radiographs which contributed to more than 50% (approximately 60,000) of the dataset to top 1000 images. We also prepared a model by adding a huge amount of noise to the augmented data, which if need be can be deployed at rural locations which lack …
Emotion Forecasting In Dyadic Conversation : Characterizing And Predicting Future Emotion With Audio-Visual Information Using Deep Learning, Sadat Shahriar
Emotion Forecasting In Dyadic Conversation : Characterizing And Predicting Future Emotion With Audio-Visual Information Using Deep Learning, Sadat Shahriar
Legacy Theses & Dissertations (2009 - 2024)
Emotion forecasting is the task of predicting the future emotion of a speaker, i.e., the emotion label of the future speaking turn–based on the speaker’s past and current audio-visual cues. Emotion forecasting systems require new problem formulations that differ from traditional emotion recognition systems. In this thesis, we first explore two types of forecasting windows(i.e., analysis windows for which the speaker’s emotion is being forecasted): utterance forecasting and time forecasting. Utterance forecasting is based on speaking turns and forecasts what the speaker’s emotion will be after one, two, or three speaking turns. Time forecasting forecasts what the speaker’s emotion will …