Digital Commons Network^™

Salty: A Diffractive Inquiry Of Visceral Knowing And Embodied Aesthetics, Mei Ling Chua

Dissertations, Theses, and Capstone Projects

This dissertation takes a diffractive, onto-epistemological approach to everyday practices with salt in order to articulate an expanded understanding of meaning making and knowledge production. This research reckons with and challenges dominant modes of knowing that engage a Cartesian perspective to situate knowing as the exclusive domain of the mind in both form and topic of inquiry. This research acts simultaneously as both a direct practice of and metacognition about knowledge production by examining 1. the embodied (including sensory and emotional aspects) and 2. the relational (including interpersonal and socio-cultural) dimensions of experience as visceral knowing. This articulation of …

Beam-Based Target Alignment For Mu2e, Helenka Casler

Dissertations, Theses, and Capstone Projects

The Mu2e Experiment is a precision experiment at Fermi National Accelerator Laboratory, searching for charged lepton flavor violation (CLFV) in the conversion of a muon to an electron in the presence of an atomic nucleus. In order to achieve the expected single-event sensitivity of 3 × 10^-17 , Mu2e will require an intense muon beam, generated via pion decay. These pions are the product of a proton beam striking a radiatively-cooled tungsten target. In order to maximize pion production and prevent target failure, the beam will have to be aligned with the target center to within 0.5 mm. The …

Naming Venus: An Exploration Of Goddesses, Heroines, And Famous Women, Kavya Beheraj

Dissertations, Theses, and Capstone Projects

Humans have been observing and romanticizing Venus for more than 5,000 years. However, mapping its surface has nearly always been impossible, since the planet is shrouded in thick clouds. A breakthrough came just fifty years ago with the invention of radar imaging, leading to the discovery (and naming) of hundreds of new features in a relatively short length of time.

The rapid naming of Venus is a case study on the impact of planetary nomenclature — the process of naming features on other worlds. While the act of naming streamlines communication and humanizes alien landscapes, it is subject to bias, …

Writing The Unwritten: Politicians, Celebrities And Activists On Lebanese Twitter, May Ahmar

Dissertations, Theses, and Capstone Projects

This dissertation explores the language and orthographic practices of different social groups on Lebanese Twitter. It draws on a specific event in Lebanon, namely the Lebanese presidential elections of 2016. The data, a collection of tweets from three different social groups (politicians, celebrities, and activists) during the month leading up to the announcement of the Lebanese president, delve into the choices of languages, orthographies and spelling that these groups exhibit on Twitter. My analysis shows that politicians use MSA mostly in alignment with their identity, linguistic hegemony and traditional media practices. Celebrities show more flexibility by choosing different languages and …

Quantifying Psychostimulant-Induced Sensitization Effects On Dopamine And Acetylcholine Release Across Different Timescales, Georg Lange

Dissertations, Theses, and Capstone Projects

Drug-induced behavioral sensitization describes the phenomenon that behavioral response to a drug of abuse is getting stronger if the same psychostimulant is delivered multiple times which is much more pronounced if done in the same environmental context. A proposed neural basis is the formation of an association between contextual cues and the rewarding drug which is mediated by dopamine. Dopamine operates at different timescales and to fully understand dopamine sensitization, it is necessary to investigate dopamine release at slow (tens of minutes) but also faster (sub-second) timescales. But creating a holistic view has been difficult due to a lack of …

Analysis Of The Inter-Annotators Agreement And Its Effect On The System Of Anti-Asian Hate Crime Detection On Twitter During Covid-19, Amir Toliyat

Dissertations, Theses, and Capstone Projects

Coronavirus disease 2019 (COVID-19) started in Wuhan, China, in late 2019, and after being utterly contagious in Asian countries, it rapidly spread to other countries. This disease caused governments worldwide to declare a public health crisis with severe measures taken to reduce the speed of the spread of the disease. This pandemic affected the lives of millions of people. Many citizens that lost their loved ones and jobs experienced a wide range of emotions, such as disbelief, shock, concerns about health, fear about food supplies, anxiety, and panic. All of the aforementioned phenomena led to the spread of racism and …

Extremism In America: Explaining Variations In Ideologically Motivated Fatal Violence, Celinet Duran

Dissertations, Theses, and Capstone Projects

This dissertation uses data from the United States Extremist Crime Database (ECDB) to assess the nature of extremist violence between left-wing, far-right and al-Qaeda and associated movements (AQAM) inspired ideological fatal violence. It extends the empirical literature on extremist violence in three significant ways by: (1) expanding an existing database to provide a comparative component that is both timely and policy-relevant and conveys a more complete picture of the nature of domestic extremism in the U.S.; (2) systematically comparing extremist violence across the left-wing, far-right and AQAM ideologies to better assess the nuances of extremist violence; and (3) applying empirical …

Political Theatre After Occupy: Participation, Interpellation, And The Search For New Subjectivities In The Theatre, Andrew J. Goldberg

Dissertations, Theses, and Capstone Projects

The use of audience participation in political theatre has become a common feature of many performances. In this dissertation, I consider the dramaturgies of a number of political theatre performances that have premiered since 2011 and demand various forms of embodied audience engagement to elucidate the potential for theatre to produce a criticality about subjectivity and the processes of subjectivation. Drawing on the recent re-engagement with Althusser’s theory of interpellation by contemporary political theorists, I develop a theory of critical aesthetic interpellation, the strategic and self-reflexive play with the theatrical apparatus to defamiliarize the process of political subjectivation. These …

Coming-Of-Age Of Teenage Female Arab Gothic Fiction: A Feminist Semiotic Study, Zoe Hurley, Zeina Hojeij

All Works

This feminist semiotic study explores the folkloric imaginary of the jinn in the context of children’s and young adults’ Arab Gothic literature. Across the Middle East, the jinn is a common trope in literature, folklore and oral storytelling who, in diegetic terms, can manifest as the Gothic figure of an aging female, deranged older woman or succubus (known as sa’lawwa in Arabic). In this study, a novel feminist semiotic framework is developed to explore the extent to which the Gothic female succubus either haunts or liberates Arab girls’ coming-of-age fictions. This issue is addressed via a feminist semiotic reading of …

Alignment-Enriched Tuning For Patch-Level Pre-Trained Document Image Models, Lei Wang, Jiabang He, Xing Xu, Ning Liu, Hui Liu

Research Collection School Of Computing and Information Systems

Alignment between image and text has shown promising im provements on patch-level pre-trained document image mod els. However, investigating more effective or finer-grained alignment techniques during pre-training requires a large amount of computation cost and time. Thus, a question natu rally arises: Could we fine-tune the pre-trained models adap tive to downstream tasks with alignment objectives and achieve comparable or better performance? In this paper, we pro pose a new model architecture with alignment-enriched tuning (dubbed AETNet) upon pre-trained document image models, to adapt downstream tasks with the joint task-specific super vised and alignment-aware contrastive objective. Specifically, weintroduce an extra …

Web Apis: Features, Issues, And Expectations: A Large-Scale Empirical Study Of Web Apis From Two Publicly Accessible Registries Using Stack Overflow And A User Survey, Neng Zhang, Ying Zou, Xin Xia, David Lo, David Lo, Shanping Li

Research Collection School Of Computing and Information Systems

With the increasing adoption of services-oriented computing and cloud computing technologies, web APIs have become the fundamental building blocks for constructing software applications. Web APIs are developed and published on the internet. The functionality of web APIs can be used to facilitate the development of software applications. There are numerous studies on retrieving and recommending candidate web APIs based on user requirements from a large set of web APIs. However, there are very limited studies on the features of web APIs that make them more likely to be used and the issues of using web APIs in practice. Moreover, users' …

Layout Generation As Intermediate Action Sequence Prediction, Huiting Yang, Danqing Huang, Chin-Yew Lin, Shengfeng He

Research Collection School Of Computing and Information Systems

Layout generation plays a crucial role in graphic design intelligence. One important characteristic of the graphic layouts is that they usually follow certain design principles. For example, the principle of repetition emphasizes the reuse of similar visual elements throughout the design. To generate a layout, previous works mainly attempt at predicting the absolute value of bounding box for each element, where such target representation has hidden the information of higher-order design operations like repetition (e.g. copy the size of the previously generated element). In this paper, we introduce a novel action schema to encode these operations for better modeling the …

Planning And Learning For Non-Markovian Negative Side Effects Using Finite State Controllers, Aishwarya Srivastava, Sandhya Saisubramanian, Praveen Paruchuri, Akshat Kumar, Shlomo Zilberstein

Research Collection School Of Computing and Information Systems

Autonomous systems are often deployed in the open world where it is hard to obtain complete specifications of objectives and constraints. Operating based on an incomplete model can produce negative side effects (NSEs), which affect the safety and reliability of the system. We focus on mitigating NSEs in environments modeled as Markov decision processes (MDPs). First, we learn a model of NSEs using observed data that contains state-action trajectories and severity of associated NSEs. Unlike previous works that associate NSEs with state-action pairs, our framework associates NSEs with entire trajectories, which is more general and captures non-Markovian dependence on states …

Mitigating Popularity Bias For Users And Items With Fairness-Centric Adaptive Recommendation, Zhongzhou Liu, Yuan Fang, Min Wu

Research Collection School Of Computing and Information Systems

Recommendation systems are popular in many domains. Researchers usually focus on the effectiveness of recommendation (e.g., precision) but neglect the popularity bias that may affect the fairness of the recommendation, which is also an important consideration that could influence the benefits of users and item providers. A few studies have been proposed to deal with the popularity bias, but they often face two limitations. Firstly, most studies only consider fairness for one side - either users or items, without achieving fairness jointly for both. Secondly, existing methods are not sufficiently tailored to each individual user or item to cope with …

Learning To Count Isomorphisms With Graph Neural Networks, Xingtong Yu, Zemin Liu, Yuan Fang, Xinming Zhang

Research Collection School Of Computing and Information Systems

Subgraph isomorphism counting is an important problem on graphs, as many graph-based tasks exploit recurring subgraph patterns. Classical methods usually boil down to a backtracking framework that needs to navigate a huge search space with prohibitive computational costs. Some recent studies resort to graph neural networks (GNNs) to learn a low-dimensional representation for both the query and input graphs, in order to predict the number of subgraph isomorphisms on the input graph. However, typical GNNs employ a node-centric message passing scheme that receives and aggregates messages on nodes, which is inadequate in complex structure matching for isomorphism counting. Moreover, on …

On Generalized Degree Fairness In Graph Neural Networks, Zemin Liu, Trung Kien Nguyen, Yuan Fang

Research Collection School Of Computing and Information Systems

Conventional graph neural networks (GNNs) are often confronted with fairness issues that may stem from their input, including node attributes and neighbors surrounding a node. While several recent approaches have been proposed to eliminate the bias rooted in sensitive attributes, they ignore the other key input of GNNs, namely the neighbors of a node, which can introduce bias since GNNs hinge on neighborhood structures to generate node representations. In particular, the varying neighborhood structures across nodes, manifesting themselves in drastically different node degrees, give rise to the diverse behaviors of nodes and biased outcomes. In this paper, we first define …

Flexible Job-Shop Scheduling Via Graph Neural Network And Deep Reinforcement Learning, Wen Song, Xinyang Chen, Qiqiang Li, Zhiguang Cao

Research Collection School Of Computing and Information Systems

Recently, deep reinforcement learning (DRL) has been applied to learn priority dispatching rules (PDRs) for solving complex scheduling problems. However, the existing works face challenges in dealing with flexibility, which allows an operation to be scheduled on one out of multiple machines and is often required in practice. Such one-to-many relationship brings additional complexity in both decision making and state representation. This article considers the well-known flexible job-shop scheduling problem and addresses these issues by proposing a novel DRL method to learn high-quality PDRs end to end. The operation selection and the machine assignment are combined as a composite decision. …

Pose- And Attribute-Consistent Person Image Synthesis, Cheng Xu, Zejun Chen, Jiajie Mai, Xuemiao Xu, Shengfeng He

Research Collection School Of Computing and Information Systems

PersonImageSynthesisaimsattransferringtheappearanceofthesourcepersonimageintoatargetpose. Existingmethods cannot handle largeposevariations and therefore suffer fromtwocritical problems: (1)synthesisdistortionduetotheentanglementofposeandappearanceinformationamongdifferentbody componentsand(2)failureinpreservingoriginalsemantics(e.g.,thesameoutfit).Inthisarticle,weexplicitly addressthesetwoproblemsbyproposingaPose-andAttribute-consistentPersonImageSynthesisNetwork (PAC-GAN).Toreduceposeandappearancematchingambiguity,weproposeacomponent-wisetransferring modelconsistingoftwostages.Theformerstagefocusesonlyonsynthesizingtargetposes,whilethelatter renderstargetappearancesbyexplicitlytransferringtheappearanceinformationfromthesourceimageto thetargetimageinacomponent-wisemanner. Inthisway,source-targetmatchingambiguityiseliminated duetothecomponent-wisedisentanglementofposeandappearancesynthesis.Second,tomaintainattribute consistency,werepresenttheinputimageasanattributevectorandimposeahigh-levelsemanticconstraint usingthisvectortoregularizethetargetsynthesis.ExtensiveexperimentalresultsontheDeepFashiondataset demonstratethesuperiorityofourmethodoverthestateoftheart,especiallyformaintainingposeandattributeconsistenciesunderlargeposevariations.

Scalable And Globally Optimal Generalized L1 K-Center Clustering Via Constraint Generation In Mixed Integer Linear Programming, Aravinth Chembu, Scott Sanner, Hassan Khurram, Akshat Kumar

Research Collection School Of Computing and Information Systems

The k-center clustering algorithm, introduced over 35 years ago, is known to be robust to class imbalance prevalent in many clustering problems and has various applications such as data summarization, document clustering, and facility location determination. Unfortunately, existing k-center algorithms provide highly suboptimal solutions that can limit their practical application, reproducibility, and clustering quality. In this paper, we provide a novel scalable and globally optimal solution to a popular variant of the k-center problem known as generalized L1 k-center clustering that uses L1 distance and allows the selection of arbitrary vectors as cluster centers. We show that this clustering objective …

Constrained Reinforcement Learning In Hard Exploration Problems, Pankayaraj Pathmanathan, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

One approach to guaranteeing safety in Reinforcement Learning is through cost constraints that are imposed on trajectories. Recent works in constrained RL have developed methods that ensure constraints can be enforced even at learning time while maximizing the overall value of the policy. Unfortunately, as demonstrated in our experimental results, such approaches do not perform well on complex multi-level tasks, with longer episode lengths or sparse rewards. To that end, wepropose a scalable hierarchical approach for constrained RL problems that employs backward cost value functions in the context of task hierarchy and a novel intrinsic reward function in lower levels …

Future Aware Pricing And Matching For Sustainable On-Demand Ride Pooling, Xianjie Zhang, Pradeep Varakantham, Hao Jiang

Research Collection School Of Computing and Information Systems

The popularity of on-demand ride pooling is owing to the benefits offered to customers (lower prices), taxi drivers (higher revenue), environment (lower carbon footprint due to fewer vehicles) and aggregation companies like Uber (higher revenue). To achieve these benefits, two key interlinked challenges have to be solved effectively: (a) pricing – setting prices to customer requests for taxis; and (b) matching – assignment of customers (that accepted the prices) to taxis/cars. Traditionally, both these challenges have been studied individually and using myopic approaches (considering only current requests), without considering the impact of current matching on addressing future requests. In this …

An Empirical Study Of Package Management Issues Via Stack Overflow, Syful Islam, Raula Kula, Christoph Treude, Bodin Chinthanet, Takashi Ishio, Kenichi Matsumoto

Research Collection School Of Computing and Information Systems

The package manager (PM) is crucial to most technology stacks, acting as a broker to ensure that a verified dependency package is correctly installed, configured, or removed from an application. Diversity in technology stacks has led to dozens of PMs with various features. While our recent study indicates that package management features of PM are related to end-user experiences, it is unclear what those issues are and what information is required to resolve them. In this paper, we have investigated PM issues faced by end-users through an empirical study of content on Stack Overflow (SO). We carried out a qualitative …

Bidding Graph Games With Partially-Observable Budgets, Guy Avni, Ismael Jecker, Dorde Zikelic

Research Collection School Of Computing and Information Systems

Two-player zero-sum graph games are a central model, which proceeds as follows. A token is placed on a vertex of a graph, and the two players move it to produce an infinite play, which determines the winner or payoff of the game. Traditionally, the players alternate turns in moving the token. In bidding games, however, the players have budgets and in each turn, an auction (bidding) determines which player moves the token. So far, bidding games have only been studied as fullinformation games. In this work we initiate the study of partial-information bidding games: we study bidding games in which …

Learning Control Policies For Stochastic Systems With Reach-Avoid Guarantees, Dorde Zikelic, Mathias Lechner, A. Thomas Henzinger, Krishnendu Chatterjee

Research Collection School Of Computing and Information Systems

We study the problem of learning controllers for discrete-time non-linear stochastic dynamical systems with formal reach-avoid guarantees. This work presents the first method for providing formal reach-avoid guarantees, which combine and generalize stability and safety guarantees, with a tolerable probability threshold p ∈ [0,1] over the infinite time horizon in general Lipschitz continuous systems. Our method leverages advances in machine learning literature and it represents formal certificates as neural networks. In particular, we learn a certificate in the form of a reach-avoid supermartingale (RASM), a novel notion that we introduce in this work. Our RASMs provide reachability and avoidance guarantees …

Safe Delivery Of Critical Services In Areas With Volatile Security Situation Via A Stackelberg Game Approach, Tien Mai, Arunesh Sinha

Research Collection School Of Computing and Information Systems

Vaccine delivery in under-resourced locations with security risks is not just challenging but also life threatening. The COVID pandemic and the need to vaccinate added even more urgency to this issue. Motivated by this problem, we propose a general framework to set-up limited temporary (vaccination) centers that balance physical security and desired (vaccine) service coverage with limited resources. We set-up the problem as a Stackelberg game between the centers operator (defender) and an adversary, where the set of centers is not fixed a priori but is part of the decision output. This results in a mixed combinatorial and continuous optimization …

An Enhanced Cloud-Native Deep Learning Pipeline For The Classification Of Network Traffic, Ahmed Sobhy Elkenawy

Theses and Dissertations

In a rapidly changing world, the way of solving real-world problems has changed to leverage the power of the advancements in multiple fields. Cloud-native computing approaches can be utilized with deep learning techniques to provide solutions in several important areas. For instance, with the emergence of the pandemic, much dependence on modern technologies came out as a replacement for face-to-face interaction. Deep learning can reach a high level of accuracy, which makes it very effective in the support of modern services and technologies. However, there are some challenging issues because deep learning requires many large-scale experiments, which demand a lot …

Utilizing Design Thinking To Enhance Wellbeing In Interdisciplinary Courses: A Positive Social Psychology Approach, Areej Elsayary

All Works

Design thinking is considered to be a positive social psychology intervention. Students who practice using the design thinking process during their coursework will most likely solve personal problems that improve their well-being. This study investigates the impact of using the design thinking process in interdisciplinary courses to enhance students' well-being through developing mindsets that enable them to solve their problems and set personal goals unconsciously. A modified PERMA-PH model is used to guide this study. The PERMA included Positive Emotions, Negative Emotions, Engagement, Relationship, Meaning and Purpose, and Accomplishments, the "PH" (future plans and health) was added to the PE …

Engineering A Microbiosphere To Clean Up The Ocean – Inspiration From The Plastisphere, Khulood A. Alnahdi, Laila W. Alali, Mezna K. Suwaidan, M. Kalim Akhtar

All Works

Plastic is a ubiquitous material that has become an essential part of our lives. More than one hundred million tons of plastic has accumulated in the world’s oceans as a result of poor waste management. This plastic waste gradually fragments into smaller pieces known as microplastics and nanoplastics. These small plastic particles can cause significant damage to marine ecosystems, and negatively impact human health. According to a recent review of international patents, the majority of ocean-cleaning inventions are limited to microplastics larger than 20 μm. Furthermore, such technologies are ineffective for nanoplastics, which measure less than 1000 nm, or even …

Carla+: An Evolution Of The Carla Simulator For Complex Environment Using A Probabilistic Graphical Model, Sumbal Malik, Manzoor Ahmed Khan, Aadam, Hesham El-Sayed, Farkhund Iqbal, Jalal Khan, Obaid Ullah

All Works

In an urban and uncontrolled environment, the presence of mixed traffic of autonomous vehicles, classical vehicles, vulnerable road users, e.g., pedestrians, and unprecedented dynamic events makes it challenging for the classical autonomous vehicle to navigate the traffic safely. Therefore, the realization of collaborative autonomous driving has the potential to improve road safety and traffic efficiency. However, an obvious challenge in this regard is how to define, model, and simulate the environment that captures the dynamics of a complex and urban environment. Therefore, in this research, we first define the dynamics of the envisioned environment, where we capture the dynamics relevant …

Independent Analysis And Interpretation Of The New Mexico Constitution: If Not Now, When?, The Honorable Linda M. Vanzi, Mark T. Baker

New Mexico Law Review

This article posits that the mode of analysis of state constitutional issues adopted by the New Mexico Supreme Court in State v. Gomez, as it has been applied in the civil context, has neither “promote[d] the development of a sound body of state constitutional jurisprudence” nor led to “consistent, predictable, and reasonable state court decision-making,” as some envisioned in 1998. Instead, it has often led to essentially automatic deference to federal precedent interpreting comparable provisions of the Federal Constitution and very limited, if any, consideration of protections afforded under our state constitution. In addition, the inconsistent application of the Gomez …