Computer Sciences Commons^™

Enhancing Security And Robustness Of Contextual Human-Centric Sensing, Jingyu Xin

Dissertations - ALL

The rapid advancements in deep learning and smart hardware have accelerated the development of various automatic human-centric sensing applications. However, the intrusive nature of these sensing applications and the heterogeneity of sensory data pose challenges in real-world deployment. While performance is crucial, ensuring the security and robustness of these applications is equally imperative for their reliable operation. To tackle the challenges associated with security and robustness in human-centric sensing, this dissertation outlines two specific objectives: (1) mitigating false data injection attacks (FDIA) on sensing applications, and (2) establishing a generalized personalization framework for human sensing models. FDIA operates by injecting …

The Hazard Prediction Problem, Mary E. Helander, Brendan Smith, Sylvia Charchut, Erika Swiatowy, Calvin Nau, Gregory Cavaretta, Timothy Schuler, Adam Schunk, Héctor Ortiz-Peña

Social Science - All Scholarship

This work formulates the hazard prediction problem while addressing the research question: Can machine learning create a model to automatically recognize patterns that correspond to hazard state conditions during a mission-critical operation? Supervised learning models were trained and tested on data observed from mission simulators, which allowed for safe observation of dynamic system states and undesirable casualty events. The prediction task was formulated as a binary classification problem, producing the probability of being in a hazard state at time t and providing situational awareness of a possible imminent loss. Several modeling architectures were investigated: neural networks, logistic regression, a support …

Ai Empire: Unraveling The Interlocking Systems Of Oppression In Generative Ai's Global Order, Jasmina Tacheva, Srividya Ramasubramanian

Media Studies - All Scholarship

As artificial intelligence (AI) continues to captivate the collective imagination through the latest generation of generative AI models such as DALL-E and ChatGPT, the dehumanizing and harmful features of the technology industry that have plagued it since its inception only seem to deepen and intensify. Far from a “glitch” or unintentional error, these endemic issues are a function of the interlocking systems of oppression upon which AI is built. Using the analytical framework of “Empire,” this paper demonstrates that we live not simply in the “age of AI” but in the age of AI Empire. Specifically, we show that …

Interpretable Network Representations, Shengmin Jin

Dissertations - ALL

Networks (or interchangeably graphs) have been ubiquitous across the globe and within science and engineering: social networks, collaboration networks, protein-protein interaction networks, infrastructure networks, among many others. Machine learning on graphs, especially network representation learning, has shown remarkable performance in network-based applications, such as node/graph classification, graph clustering, and link prediction. Like performance, it is equally crucial for individuals to understand the behavior of machine learning models and be able to explain how these models arrive at a certain decision. Such needs have motivated many studies on interpretability in machine learning. For example, for social network analysis, we may need …

Algorithmic Solutions To Combat Online Fake News, Xinyi Zhou

Dissertations - ALL

The unprecedented growth of new information producing, distributing, and consuming every moment on the Web has fostered the rise of ``fake news.'' Because of its detrimental effect on democracy, global economies, and public health, effectively combating online fake news has become an essential and urgent task.

This dissertation starts with making typological, theoretical, and empirical efforts to promote the public's comprehension of fake news and lay the foundation for algorithmically combating fake news. As there has been no universal definition of fake news, this dissertation discusses the definition of fake news from three dimensions: veracity, intention, and news, comparing it …

Protection Against Contagion In Complex Networks, Pegah Hozhabrierdi

Dissertations - ALL

In real-world complex networks, harmful spreads, commonly known as contagions, are common and can potentially lead to catastrophic events if uncontrolled. Some examples include pandemics, network attacks on crucial infrastructure systems, and the propagation of misinformation or radical ideas. Thus, it is critical to study the protective measures that inhibit or eliminate contagion in these networks. This is known as the network protection problem.

The network protection problem investigates the most efficient graph manipulations (e.g., node and/or edge removal or addition) to protect a certain set of nodes known as critical nodes. There are two types of critical nodes: (1) …

Adversarial Activity Detection And Prediction Using Behavioral Biometrics, Amin Fallahi

Dissertations - ALL

Behavioral biometrics can be used in different security applications like authentication, identification, etc. One of the trending applications is predicting future activities of people and guessing whether they will engage in malicious activities in the future. In this research, we study the possibility of predicting future activities and propose novel methods for near-future activity prediction.

First, we study gait signals captured using smartphone accelerometer sensor and build a model to predict a future gait signal. Activity recognition using body movements captured from mobile phone sensors has been a major point of interest in recent research. Data that is being continuously …

Understanding And Hardening Blockchain Network Security Against Denial Of Service Attacks, Kai Li

Dissertations - ALL

This thesis aims to examine the security of a blockchain's communication network. A blockchain relies on a communication network to deliver transactions. Understanding and hardening the security of the communication network against Denial-of-Service (DoS) attacks are thus critical to the well-being of blockchain participants. Existing research has examined blockchain system security in various system components, including mining incentives, consensus protocols, and applications such as smart contracts. However, the security of a blockchain's communication network remains understudied.

In practice, a blockchain's communication network typically consists of three services: RPC service, P2P network, and mempool. This thesis examines each service's designs and …

Fairness In Social Networks, Zeinab Saghati Jalali

Dissertations - ALL

In professional and other social settings, networks play an important role in people's lives. The communication between individuals and their positions in the network, may have a large impact on many aspects of their lives.In this work, I evaluate fairness from different perspectives.First,tomeasurefairnessfromgroupperspective,Iproposethenovelinformation unfairness criterion, which measures whether information spreads fairly to different groups in a network. Using this criterion, I perform a case study and measure fairness in information flow in different computer science co-authorship networks with respect to gender. Then, I consider two applications and show how to increase fairness with respect to a fairness metric. The first …

Seedemu: The Seed Internet Emulator, Honghao Zeng

Theses - ALL

I studied and experimented with the idea of building an emulator for the Internet. While there are various already available options for such a task, none of them takes the emulation of the entire Internet as an important feature in mind. Those emulators and simulators can handle small-scale networks pretty well, but lacks the ability to handle large-size networks, mainly due to:

- Not being able to run many nodes, or requires very powerful hardware to do so,- Lacks convenient ways to build a large emulation, and - Lacks reusability: once something is built, it is very hard to re-use …

Computer Vision Applications For Autonomous Aerial Vehicles, Burak Kakillioglu

Dissertations - ALL

Undoubtedly, unmanned aerial vehicles (UAVs) have experienced a great leap forward over the last decade. It is not surprising anymore to see a UAV being used to accomplish a certain task, which was previously carried out by humans or a former technology. The proliferation of special vision sensors, such as depth cameras, lidar sensors and thermal cameras, and major breakthroughs in computer vision and machine learning fields accelerated the advance of UAV research and technology. However, due to certain unique challenges imposed by UAVs, such as limited payload capacity, unreliable communication link with the ground stations and data safety, UAVs …

Mathematical Optimization Algorithms For Model Compression And Adversarial Learning In Deep Neural Networks, Tianyun Zhang

Dissertations - ALL

Large-scale deep neural networks (DNNs) have made breakthroughs in a variety of tasks, such as image recognition, speech recognition and self-driving cars. However, their large model size and computational requirements add a significant burden to state-of-the-art computing systems. Weight pruning is an effective approach to reduce the model size and computational requirements of DNNs. However, prior works in this area are mainly heuristic methods. As a result, the performance of a DNN cannot maintain for a high weight pruning ratio. To mitigate this limitation, we propose a systematic weight pruning framework for DNNs based on mathematical optimization. We first formulate …

Treadmill Assisted Circumvention Of Wearable Sensors-Based Gait Authentication, Rajesh Kumar

Dissertations - ALL

Wearable sensor-based gait patterns are considered a promising means for future authentication systems. This dissertation examines whether circumvention of such systems can be accomplished by imitating sensor readings and providing external mechanical support. The specific machine that we used in the experiment was a digital treadmill, which provides a suitable platform for the human imitators to control, adjust and adapt several factors, such as speed, step-length, step-width, and thigh-lift that affect sensor readings. Moreover, it was easy for imitators to remember the gait factors' specific levels and repeat the learned pattern on-demand over a treadmill.

Two novel imitation-based attacks are …

Real-Time Adaptive Sensor Attack Detection And Recovery In Autonomous Cyber-Physical Systems, Francis Akowuah

Dissertations - ALL

Cyber-Physical Systems (CPS) tightly couple information technology with physical processes, which rises new vulnerabilities such as physical attacks that are beyond conventional cyber attacks.Attackers may non-invasively compromise sensors and spoof the controller to perform unsafe actions. This issue is even emphasized with the increasing autonomy in CPS. While this fact has motivated many defense mechanisms against sensor attacks, a clear vision of the timing and usability (or the false alarm rate) of attack detection still remains elusive. Existing works tend to pursue an unachievable goal of minimizing the detection delay and false alarm rate at the same time, while there …

Augmented Human Machine Intelligence For Distributed Inference, Baocheng Geng

Dissertations - ALL

With the advent of the internet of things (IoT) era and the extensive deployment of smart devices and wireless sensor networks (WSNs), interactions of humans and machine data are everywhere. In numerous applications, humans are essential parts in the decision making process, where they may either serve as information sources or act as the final decision makers. For various tasks including detection and classification of targets, detection of outliers, generation of surveillance patterns and interactions between entities, seamless integration of the human and the machine expertise is required where they simultaneously work within the same modeling environment to understand and …

Enhancing Usability Of Malware Analysis Pipelines With Reverse Engineering, Jeffrey Ching

Theses - ALL

Lots of work has been done on analyzing software distributed in binary form. This is a challenging problem because of the relatively unstructured nature of binaries. To recover high-level structure, various attempts have included static and dynamic analysis. However, human inspection is often required, as high-level structure is compiled away. Recent success in this area includes work on variable-name recovery, vulnerability discovery, class recovery for object-oriented languages. We are interested in building a pipeline for user to analyze malware. In this thesis we tackle two problems central to malware analysis pipelines. The first is D3RE, an interactive querying tool that …

Sentiment Classification Bias In User Generated Content, Alpana Deshpande

Theses - ALL

Interactive websites generate terabytes of data on a daily basis. This data canbe used in multiple analytical applications to teach computers more about human behavior. Text classification is such an application. Multiple freely available user-generated text data can be used to teach computers to identify the sentiments behind a user's on-screen interactions without the need of any human intervention. Sentiment analysis is an interesting problem, solving which would theoretically get a computer closer to passing the Turing test. Through this thesis, we test the ability of a classifier to accurately identify user sentiments. However, we do not focus on standard …

Inferring Degree Of Localization Of Twitter Persons And Topics Through Time, Language, And Location Features, Aleksey Valeriy Panasyuk

Dissertations - ALL

Identifying authoritative influencers related to a geographic area (geo-influencers) can aid content recommendation systems and local expert finding. This thesis addresses this important problem using Twitter data.

A geo-influencer is identified via the locations of its followers. On Twitter, due to privacy reasons, the location reported by followers is limited to profile via a textual string or messages with coordinates. However, this textual string is often not possible to geocode and less than 1\% of message traffic provides coordinates. First, the error rates associated with Google's geocoder are studied and a classifier is built that gives a warning for self-reported …

Experience-Driven Control For Networking And Computing, Zhiyuan Xu

Dissertations - ALL

Modern networking and computing systems have become very complicated and highly dynamic, which makes them hard to model, predict and control. In this thesis, we aim to study system control problems from a whole new perspective by leveraging emerging Deep Reinforcement Learning (DRL), to develop experience-driven model-free approaches, which enable a network or a device to learn the best way to control itself from its own experience (e.g., runtime statistics data) rather than from accurate mathematical models, just as a human learns a new skill (e.g., driving, swimming, etc). To demonstrate the feasibility and superiority of this experience-driven control design …

Sentiment Classification Bias In User Generated Content, Alpana Deshpande

Theses - ALL

Interactive websites generate terabytes of data on a daily basis. This data canbe used in multiple analytical applications to teach computers more about human behavior. Text classification is such an application. Multiple freely available user-generated text data can be used to teach computers to identify the sentiments behind a user’s on-screen interactions without the need of any human intervention. Sentiment analysis is an interesting problem, solving which would theoretically get a computer closer to passing the Turing test. Through this thesis, we test the ability of a classifier to accurately identify user sentiments. However, we do not focus on standard …

Enhancing Usability Of Malware Analysis Pipelines With Reverse Engineering, Jeffrey Ching

Theses - ALL

Lots of work has been done on analyzing software distributed in binary form. This is a challenging problem because of the relatively unstructured nature of binaries. To recover high-level structure, various attempts have included static and dynamic analysis. However, human inspection is often required, as high-level structure is compiled away. Recent success in this area includes work on variable-name recovery, vulnerability discovery, class recovery for object-oriented languages. We are interested in building a pipeline for user to analyze malware. In this thesis we tackle two problems central to malware analysis pipelines. The first is D3RE, an interactive querying tool that …

Inferring Degree Of Localization Of Twitter Persons And Topics Through Time, Language, And Location Features, Aleksey Valeriy Panasyuk

Dissertations - ALL

Identifying authoritative influencers related to a geographic area (geo-influencers) can aid content recommendation systems and local expert finding. This thesis addresses this important problem using Twitter data.

A geo-influencer is identified via the locations of its followers. On Twitter, due to privacy reasons, the location reported by followers is limited to profile via a textual string or messages with coordinates. However, this textual string is often not possible to geocode and less than 1\% of message traffic provides coordinates. First, the error rates associated with Google's geocoder are studied and a classifier is built that gives a warning for self-reported …

Experience-Driven Control For Networking And Computing, Zhiyuan Xu

Dissertations - ALL

Modern networking and computing systems have become very complicated and highly dynamic, which makes them hard to model, predict and control. In this thesis, we aim to study system control problems from a whole new perspective by leveraging emerging Deep Reinforcement Learning (DRL), to develop experience-driven model-free approaches, which enable a network or a device to learn the best way to control itself from its own experience (e.g., runtime statistics data) rather than from accurate mathematical models, just as a human learns a new skill (e.g., driving, swimming, etc). To demonstrate the feasibility and superiority of this experience-driven control design …

Assessing Topical Homogeneity With Word Embedding And Distance Matrices, Jeffrey M. Stanton, Yisi Sang

School of Information Studies - Faculty Scholarship

Researchers from many fields have used statistical tools to make sense of large bodies of text. Many tools support quantitative analysis of documents within a corpus, but relatively few studies have examined statistical characteristics of whole corpora. Statistical summaries of whole corpora and comparisons between corpora have potential application in the analysis of topically organized applications such social media platforms. In this study, we created matrix representations of several corpora and examined several statistical tests to make comparisons between pairs of corpora with respect to the topical homogeneity of documents within each corpus. Results of three experiments suggested that a …

Virtual Reality: A Tool Used To Engage Youth In The Voting Process In Newly Democratic Tunisia, Raed Ghanja

International Programs

Tunisia is facing a hard time with its transition to democracy partly due to the reluctance on the part of young people to participate in elections. This poster discusses why the Tunisian government must engage youth and encourage their participation in the election process by using fun simulations of this process in Virtual Reality.

Iot-Enabled Eldercare Technology, Watchanan Chantapakul

International Programs

The world’s population is changing as people are growing older. Leveraging technology for eldercare is important in this century. It can enable many eldercare applications effectively.

Hail Detection Using Dual Polarization Weather Radar, Alfonso Ladino Rincon

International Programs

This poster highlights how active remote sensors such as weather radar are completely useful for hail detection given its feature and the information they produce. Hail detection is already well studied by the atmospheric scientific community and dual polarimetric variables values for hail signature are presented according to those advances. Then, a supervised classification technique is showed to illustrated how machine learning can be integrated to radar information for automatic hail detection. However, this fuzzy logic algorithm has the capability to distinguish between meteorological and non-meteorological echoes. This automatic information might help forecasters from National Weather Services – NWS to …

A Bystander's Dilemma: Participatory Design Study Of Privacy Expectations For Smart Home Devices, Oriana Mcdonough

Renée Crown University Honors Thesis Projects - All

Traditional homes have become increasingly filled with Internet-connected devices, turning them into “smart homes.” Currently, research around privacy concerns with smart home devices has focused on the end users. The goal for our research is to understand the perceptions and desired privacy mechanisms from the perspective of a different stakeholder, i.e., the bystanders. Bystanders in this context are individuals who are not the owner or primary user of smart home devices but are potentially affected by the device usage, such as house guests or family members. In order to understand this, we conducted a focus group study with co-design activities …

The Golden Ticket: How Blockchain Technology Can Be Implemented Into Event Ticketing, Jack Singer

Renée Crown University Honors Thesis Projects - All

When the group/individual named Satoshi Nakamoto first conceptualized blockchain in 2008, it served as the underlying foundation to the cryptocurrency Bitcoin. In the years following, cryptocurrencies alike experiences massive gains in profitability; however, after the bubble had burst organizations began to look at the technology from a more academic standpoint. It was quickly found out that there is a massive application for blockchain in almost all sectors of industry from bulk stores (Walmart) to banking (IBM). This paper will explore how blockchain technology can be implemented into event ticketing, more specifically concerts. The current landscape of the industry is under …

Exploring Data Science: Understanding, Predicting, & Visualizing Crime In Syracuse, Ryan French

Renée Crown University Honors Thesis Projects - All

With the advent of the open data portal for the city of Syracuse came an opportunity previously impossible; anyone could download, mine, and visualize information about Syracuse direct from the source. Over the course of this project, I will be performing these processes on a selection of crime data from 2017 in order to better understand the patterns of crime in Syracuse, where they occur, and if it can be predicted whether or not a crime will lead to an arrest.

This project will begin with an overview of the data, how it was obtained, and the meanings of the …