Computer Sciences Commons^™

Curiosity-Driven And Victim-Aware Adversarial Policies, Chen Gong, Zhou Yang, Yunpeng Bai, Jieke Shi, Arunesh Sinha, Bowen Xu, David Lo, Xinwen Hou, Guoliang Fan

Research Collection School Of Computing and Information Systems

Recent years have witnessed great potential in applying Deep Reinforcement Learning (DRL) in various challenging applications, such as autonomous driving, nuclear fusion control, complex game playing, etc. However, recently researchers have revealed that deep reinforcement learning models are vulnerable to adversarial attacks: malicious attackers can train adversarial policies to tamper with the observations of a well-trained victim agent, the latter of which fails dramatically when faced with such an attack. Understanding and improving the adversarial robustness of deep reinforcement learning is of great importance in enhancing the quality and reliability of a wide range of DRL-enabled systems. In this paper, …

Supply Regulation Under The Exclusion Policy In A Ride-Sourcing Market, Xiaonan Li, Xiangyong Li, Hai Wang, Junxin Shi, Yash P. Aneja

Research Collection School Of Computing and Information Systems

On-demand ride-sourcing platforms have quickly emerged and become ubiquitous in our daily lives. Motivated by the rising public concern about service quality in the ride-sourcing market, this paper aims to examine the impact of exclusion policy that can serve as both quality management and supply regulation strategy. With an exclusion policy, the platform excludes low-quality service providers/drivers from the ride-sourcing market by setting a quality threshold of admission (QTA). We propose a model to describe and analyze the market equilibrium under the exclusion policy and present our analytical and numerical results – some of which are non-intuitive and intriguing. Considering …

Question-Attentive Review-Level Recommendation Explanation, Trung Hoang Le, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Recommendation explanations help to improve their acceptance by end users. The form of explanation of interest here is presenting an existing review of the recommended item. The challenge is in selecting a suitable review, which is customarily addressed by assessing the relative importance of each review to the recommendation objective. Our focus is on improving review-level explanation by leveraging additional information in the form of questions and answers (QA). The proposed framework employs QA in an attention mechanism that aligns reviews to various QAs of an item and assesses their contribution jointly to the recommendation objective. The benefits are two-fold. …

Make Your Own Sprites: Aliasing-Aware And Cell-Controllable Pixelization, Zongwei Wu, Liangyu Chai, Nanxuan Zhao, Bailin Deng, Yongtuo Liu, Qiang Wen, Junle Wang, Shengfeng He

Research Collection School Of Computing and Information Systems

Pixel art is a unique art style with the appearance of low resolution images. In this paper, we propose a data-driven pixelization method that can produce sharp and crisp cell effects with controllable cell sizes. Our approach overcomes the limitation of existing learning-based methods in cell size control by introducing a reference pixel art to explicitly regularize the cell structure. In particular, the cell structure features of the reference pixel art are used as an auxiliary input for the pixelization process, and for measuring the style similarity between the generated result and the reference pixel art. Furthermore, we disentangle the …

Cyber Resilience Analytics For Cyber-Physical Systems, Md Ariful Haque

Electrical & Computer Engineering Theses & Dissertations

Cyber-physical systems (CPSs) are complex systems that evolve from the integrations of components dealing with physical processes and real-time computations, along with networking. CPSs often incorporate approaches merging from different scientific fields such as embedded systems, control systems, operational technology, information technology systems (ITS), and cybernetics. Today critical infrastructures (CIs) (e.g., energy systems, electric grids, etc.) and other CPSs (e.g., manufacturing industries, autonomous transportation systems, etc.) are experiencing challenges in dealing with cyberattacks. Major cybersecurity concerns are rising around CPSs because of their ever-growing use of information technology based automation. Often the security concerns are limited to probability-based possible attack …

A Relevance Model For Threat-Centric Ranking Of Cybersecurity Vulnerabilities, Corren G. Mccoy

Computer Science Theses & Dissertations

The relentless and often haphazard process of tracking and remediating vulnerabilities is a top concern for cybersecurity professionals. The key challenge they face is trying to identify a remediation scheme specific to in-house, organizational objectives. Without a strategy, the result is a patchwork of fixes applied to a tide of vulnerabilities, any one of which could be the single point of failure in an otherwise formidable defense. This means one of the biggest challenges in vulnerability management relates to prioritization. Given that so few vulnerabilities are a focus of real-world attacks, a practical remediation strategy is to identify vulnerabilities likely …

Hard-Real-Time Computing Performance In A Cloud Environment, Alvin Cornelius Murphy

Engineering Management & Systems Engineering Theses & Dissertations

The United States Department of Defense (DoD) is rapidly working with DoD Services to move from multi-year (e.g., 7-10) traditional acquisition programs to a commercial industrybased approach for software development. While commercial technologies and approaches provide an opportunity for rapid fielding of mission capabilities to pace threats, the suitability of commercial technologies to meet hard-real-time requirements within a surface combat system is unclear. This research establishes technical data to validate the effectiveness and suitability of current commercial technologies to meet the hard-real-time demands of a DoD combat management system. (Moreland Jr., 2013) conducted similar research; however, microservices, containers, and container …

Towards Privacy And Security Concerns Of Adversarial Examples In Deep Hashing Image Retrieval, Yanru Xiao

Computer Science Theses & Dissertations

With the explosive growth of images on the internet, image retrieval based on deep hashing attracts spotlights from both research and industry communities. Empowered by deep neural networks (DNNs), deep hashing enables fast and accurate image retrieval on large-scale data. However, inheriting from deep learning, deep hashing remains vulnerable to specifically designed input, called adversarial examples. By adding imperceptible perturbations on inputs, adversarial examples fool DNNs to make wrong decisions. The existence of adversarial examples not only raises security concerns for real-world deep learning applications, but also provides us with a technique to confront malicious applications.

In this dissertation, we …

Machine Learning-Based Event Generator, Yasir Alanazi

Computer Science Theses & Dissertations

Monte Carlo-based event generators have been the primary source for simulating particle collision experiments for the study of interesting physics scenarios. Monte Carlo generators rely on theoretical assumptions, which limit their ability to capture the full range of possible correlations between particle’s momenta. In addition, the simulations of the complete pipeline often take minutes to generate a single event even with the help of supercomputers.

In recent years, much attention has been devoted to the development of machine learning event generators. They demonstrate attractive advantages, including fast simulations, data compression, and being agnostic of theoretical assumptions. However, most of the …

Drug Abuse Ontology To Harness Web-Based Data For Substance Use Epidemiology Research, Usha Lokala, Francois Lamy, Raminta Daniulaityte, Manas Gaur, Amelie Gyrard, Krishnaprasad Thirunarayan, Ugur Kursuncu, Amit Sheth

Computer Science and Engineering Faculty Publications

Background: Web-based resources and social media platforms play an increasingly important role in health-related knowledge and experience sharing. There is a growing interest in the use of these novel data sources for epidemiological surveillance of substance use behaviors and trends. Objective: The key aims were to describe the development and application of the drug abuse ontology (DAO) as a framework for analyzing web-based and social media data to inform public health and substance use research in the following areas: determining user knowledge, attitudes, and behaviors related to nonmedical use of buprenorphine and illicitly manufactured opioids through the analysis of web …

Data From: Machine Learning Predictions Of Electricity Capacity, Marcus Harris, Elizabeth Kirby, Ameeta Agrawal, Rhitabrat Pokharel, Francis Puyleart, Martin Zwick

Complex Systems Faculty Datasets

This research applies machine learning methods to build predictive models of Net Load Imbalance for the Resource Sufficiency Flexible Ramping Requirement in the Western Energy Imbalance Market. Several methods are used in this research, including Reconstructability Analysis, developed in the systems community, and more well-known methods such as Bayesian Networks, Support Vector Regression, and Neural Networks. The aims of the research are to identify predictive variables and obtain a new stand-alone model that improves prediction accuracy and reduces the INC (ability to increase generation) and DEC (ability to decrease generation) Resource Sufficiency Requirements for Western Energy Imbalance Market participants. This …

Differentiate Metasploit Framework Attacks From Others, Gina Liu Ajero

Electronic Theses and Dissertations

Metasploit Framework is a very popular collection of penetration testing tools. From auxiliaries such as network scanners and mappers to exploits and payloads, Metasploit Framework offers a plethera of apparatuses to implement all the stages of a penetration test. There are two versions: both a free open-source community version and a commercial professional version called Metasploit Pro. The free version, Metasploit Framework, is heavily used by cyber crimininals to carry out illegal activities to gain unauthorized access to targets.

In this paper, I conduct experiments in a virtual environment to discover whether attacks originated from Metasploit Framework are marked with …

Disease Recognition In X-Ray Images With Doctor Consultation-Inspired Model, Kim Anh Phung, Thuan Trong Nguyen, Nileshkumar Wangad, Samah Baraheem, Nguyen D. Vo, Khang Nguyen

Computer Science Faculty Publications

The application of chest X-ray imaging for early disease screening is attracting interest from the computer vision and deep learning community. To date, various deep learning models have been applied in X-ray image analysis. However, models perform inconsistently depending on the dataset. In this paper, we consider each individual model as a medical doctor. We then propose a doctor consultation-inspired method that fuses multiple models. In particular, we consider both early and late fusion mechanisms for consultation. The early fusion mechanism combines the deep learned features from multiple models, whereas the late fusion method combines the confidence scores of all …

Data-Driven Deep Learning-Based Analysis On Thz Imaging, Haoyan Liu

Graduate Theses and Dissertations

Breast cancer affects about 12.5% of women population in the United States. Surgical operations are often needed post diagnosis. Breast conserving surgery can help remove malignant tumors while maximizing the remaining healthy tissues. Due to lacking effective real-time tumor analysis tools and a unified operation standard, re-excision rate could be higher than 30% among breast conserving surgery patients. This results in significant physical, physiological, and financial burdens to those patients. This work designs deep learning-based segmentation algorithms that detect tissue type in excised tissues using pulsed THz technology. This work evaluates the algorithms for tissue type classification task among freshly …

From Computer Curriculum That Works For The Use Of Computer Intellignece Computer Science, Malachi B. Bacchus

Publications and Research

Computer interconnection can link different networks by using electrical artificial flow ways that can travel through different connections. these are called data network which travels through different sectors of the network simulation of service computer network using artificial intelligence to enhanced further understanding the computations, I've also demonstrated knowing by using the network to get better understanding of how ethical computing can be learned through universities and collegiate that can help established knowledge and healthy computer information. The main tools for the research are using data networking, ethical learning and translation towards different computer systems.

Synthetic Data Generation For Intelligent Inspection Of Structural Environments, Noshin Habib

Open Access Theses & Dissertations

Automated detection of cracks and corrosion in pavements and industrial settings is essential to a cost-effective approach to maintenance. Deep learning has paved the path for vast levels of improvement in the area. Such models require a plethora of data with accurate ground truth and enough variation for the model to generalize to the data, which is notwidely available. There has been recent progress in computer graphics being used for the creation of synthetic data to address the issue of deficient data availability, but it is limited to specific objects, such as cars and human beings. Textures and deformities within …

Identity Term Sampling For Measuring Gender Bias In Training Data, Nasim Sobhani, Sarah Jane Delany

Conference Papers

Predictions from machine learning models can reflect biases in the data on which they are trained. Gender bias has been identified in natural language processing systems such as those used for recruitment. The development of approaches to mitigate gender bias in training data typically need to be able to isolate the effect of gender on the output to see the impact of gender. While it is possible to isolate and identify gender for some types of training data, e.g. CVs in recruitment, for most textual corpora there is no obvious gender label. This paper proposes a general approach to measure …

Bevers: A General, Simple, And Performant Framework For Automatic Fact Verification, Mitchell Dehaven

School of Computing: Dissertations, Theses, and Student Research

Fact verification has become an important process, primarily done manually by humans, to verify the authenticity of claims and statements made online. Increasingly, social media companies have utilized human effort to debunk false claims on their platforms, opting to either tag the content as misleading or false, or removing it entirely to combat misinformation on their sites. In tandem, the field of automatic fact verification has become a subject of focus among the natural language processing (NLP) community, spawning new datasets and research. The most popular dataset is the Fact Extraction and VERification (FEVER) dataset. In this thesis an end-to-end …

Representation Learning For Open Set Recognition And Novel Category Discovery, Jingyun Jia

Theses and Dissertations

As machine learning models have achieved great success in various research and industry fields, the success of these models heavily relies on the massive amount of data collection and human annotations. While the real world is an open set, the daily emerged categories and the lacking of annotations have become new challenges for machine learning models. The absence of newly emerged categories in training samples can be captured by Open Set Recognition (OSR). Then, given the newly emerged samples, the process of automatically identifying the novel categories is called Novel Category Discovery (NCD). In this dissertation, we focused on learning …

The Rise Of Terrorism In Africa And What It Means For U.S. Policymakers, Bridget Mary Hughes

Master's Theses

This thesis utilizes three case studies to measure the rise of terrorism in Africa. This qualitative data leads the reader to one ultimate question: what does the rise in terrorism in Africa mean for the United States (U.S.) and U.S. policymakers, National Security academia, students in National Security, if anything at all? The answer is: if civilian and military compounds are being targeted by these violent extremists, as these three case studies demonstrate, it is my recommendation that U.S. policymakers cease sending troops to the African theater and, instead, allocate funding to the citizens and nonprofits of the countries whom …

Watching The Watchmen: An Ethical Evaluation Of The Behavior Of Modern Software Applications, Joshua Graves

University Honors Program Senior Projects

Software has become a ubiquitous element of modern life around the world. An unprecedented amount of power is bestowed upon the companies that own and operate that software. The obvious question arises: “Do these companies operate in an ethical manner regarding their software?” We derive an ethical code via synthesizing the ethical codes of both the IEEE and the ACM, disregarding principles that cannot be examined by an outside observer. We utilize this ethical code to examine five leaders in the software industry, namely Facebook, Google, Microsoft, Twitter, and Amazon. For each company, we examine four incidents in which they …

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 …

Improving Detection And Classification Of Steady-State Visual Evoked Potentials In Brain-Computer Interfaces, Hadi Habibzadeh

Legacy Theses & Dissertations (2009 - 2024)

Brain-computer interfaces (BCIs) provide an alternative communication channel to send computer commands without requiring any voluntary muscle control. They benefit people with limited communication capabilities due to motor function deficits, spinal cord injuries, and neurological disorders. They may also have applications for healthy individuals.

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 …

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 …

Addressing Human Error Through Effective Cyber Policy Design, Katherine Amoresano

Emergency Preparedness, Homeland Security, and Cybersecurity

Human error is a significant contributing factor to the rise in Cybersecurity attacks regardless of increased technical control implemented to safeguard Information systems. Adversaries can circumvent technical safeguards due to human errors which result from inadequate enforceable policies and training on Cybersecurity for the everyday user. Several studies and articles show that the majority of successful attacks are human enabled, proving the need for human-centric cybersecurity research and practices. This exploratory work reviews the human aspect of Cybersecurity by investigating the cybersecurity policies at SUNY Albany and other SUNY institutions. We used a survey of students and faculty members at …

Analyzing Academic Self-Regulation Improvement Via Proccoli Task And Time Management Application, Le Jie Bennett

Computer Science

In the academic context, success is dependent on the individual’s ability to keep track of what needs to be done in a timely manner. But, for most students, procrastination often stands in their way. The time management application, Proccoli, is a tool that the Sahebi/Feyzi lab has developed to study individual and group procrastination behavior and help improve students’ ability to track and manage their tasks and time. Drawing foundations from Human Computer Interaction literature, the application provided features and functionalities to help promote selfregulatory behaviors such as self-observation, self-judgement, and self-reflection. An evaluation survey was sent to previous Proccoli …

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 …

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.

Movie Reviews Sentiment Analysis Using Bert, Gibson Nkhata

Graduate Theses and Dissertations

Sentiment analysis (SA) or opinion mining is analysis of emotions and opinions from texts. It is one of the active research areas in Natural Language Processing (NLP). Various approaches have been deployed in the literature to address the problem. These techniques devise complex and sophisticated frameworks in order to attain optimal accuracy with their focus on polarity classification or binary classification. In this paper, we aim to fine-tune BERT in a simple but robust approach for movie reviews sentiment analysis to provide better accuracy than state-of-the-art (SOTA) methods. We start by conducting sentiment classification for every review, followed by computing …