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Description
ASU’s Software Engineering (SER) program adequately prepares students for what happens after they become a developer, but there is no standard for preparing students to secure a job post-graduation in the first place. This project creates and executes a supplemental curriculum to prepare students for the technical interview process. The trial run of the curriculum was received positively by study participants, who experienced an increase in confidence over the duration of the workshop.
ContributorsSchmidt, Julia J (Author) / Roscoe, Rod (Thesis director) / Bansal, Srividya (Committee member) / Software Engineering (Contributor) / Human Systems Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
DescriptionThe goal of this study is to equip administrators and instructors with a deeper understanding of the apparent cheating problem in Computer Science courses, with proposed solutions to lower academic dishonesty from the students’ perspective.
ContributorsAl Yasari, Farah (Co-author) / Alyasari, Farah (Co-author) / Tadayon-Navabi, Farideh (Thesis director) / Bazzi, Rida (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
ContributorsRainbow Connection Choir (Performer) / ASU Library. Music Library (Publisher)
Created2017-12-03
Description
In the United States, the word "earthquake" is extensively used. This natural disaster has a year-round impact on numerous states across the country. Earthquakes are simply more than a natural calamity; they also have a negative psychological impact. Earthquake safety measures are essential for ensuring citizens' safety. This paper proposes, a technique for evaluating earthquake safety activities and instructing individuals in selecting appropriate precautions. Earthquake protection using Reach.love plus Amazon Alexa is special in that it uses cutting-edge virtual reality technology. The platform developed by Reach.love takes earthquake prevention to a new and innovative direction. The feeling of presence in a VR headset linked within Reach.love, allows the user to feel that an earthquake is occurring right now. Additionally, each location includes audio instructions that explain what to do in specific scenarios. The user can practice and mentally train to respond appropriately when a real earthquake happens, comparable to a 3D drill. Finally, the user will be able to utilize Amazon Alexa for help within the rooms in Reach.love to improve the experience of earthquake safety training. For example, if the user speaks to Alexa during the simulation and says, "Alexa, turn off the audio instructions," Alexa will do so, and the user will no longer hear them. Alexa would be the user's personal assistant during the training of earthquake protection.
ContributorsKaur, Simran (Author) / Johnson, Mina (Thesis director) / de la Pena, Nonny (Committee member) / Barrett, The Honors College (Contributor) / Computer Science - BS (Contributor)
Created2022-05
Description
With the advent of new mobility services and technologies, the complexity of understanding the mobility patterns has been gradually intensified. The availability of large datasets, in conjunction with the transportation revolution, has been increased and incurs high computing costs. These two critical challenges require us to methodologically handle complex transportation problems with numerical performance: fast, high-precision solutions, and reliable structure under different impact factors. That is, it is imperative to introduce a new type of modeling strategy, advancing the conventional transportation planning models. In order to do this, we leverage the backbone of the underlying algorithm behind machine learning (ML): computational graph (CG) and automatic differentiation (AD). CG is a directed acyclic graph (DAG) where each vertex represents a mathematical operation, and each edge represents data transfer. AD is an efficient algorithm to analytically compute gradients of necessary functionality. Embedding the two key algorithms into the planning models, specifically parametric-based econometric models and network optimization models, we theoretically and practically develop different types of modeling structures and reformulate mathematical formulations on basis of the graph-oriented representation.
Three closely related analytical and computational frameworks are presented in this dissertation, based on a common modeling methodology of CG abstraction. First, a two-stage interpretable machine learning framework developed by a linear regression model, coupled with a neural network layered by long short-term memory (LSTM) shows the capability of capturing statistical characteristics with enhanced predictability in the context of day-to-day streaming datasets. Second, AD-based computation in estimating for discrete choice models proves more efficiency of handling complex modeling structure than the standard optimization solver relying on numerical gradients, outperforming the standard methods, Biogeme and Apollo. Lastly, CG allows modelers to take advantage of a special problem structure for the feedback loops, a new class of problem reformulation developed through Lagrangian relaxation (LR), which makes CG based model well suited for reaching a high degree of the integrated demand-supply consistency.
Overall, the deep integration of the practically important planning models with the underlying computationally efficient ML algorithms can enhance behavioral understanding of interactions in real-world urban systems, and the proposed differentiable mathematical structures will enable transportation decision-makers to accurately evaluate different demand-side and supply-side scenarios with a higher degree of convergency and optimality in more complex transportation systems.
ContributorsKim, Taehooie (Author) / Pendyala, Ram RP (Thesis advisor) / Zhou, Xuesong XZ (Thesis advisor) / Pan, Rong RP (Committee member) / Lou, Yingyan YL (Committee member) / Arizona State University (Publisher)
Created2021
Description
Mobile Augmented Reality (MAR) is a portable, powerful, and suitable technology that integrates 3D virtual content into the physical world in real-time. It has been implemented for multiple intents as it enhances people’s interaction, e.g., shopping, entertainment, gaming, etc. Thus, MAR is expected to grow at a tremendous rate in the upcoming years, as its popularity via mobile devices has increased. But, unfortunately, the applications that implement MAR, hereby referred to as MAR-Apps, bear security issues. Such are imaged in worldwide recorded incidents caused by MAR-Apps, e.g., robberies, authorities requesting banning MAR at specific locations, etc. To further explore these concerns, a case study analyzed several MAR-Apps available in the market to identify the security problems in MAR. As a result of this study, the threats found were classified into three categories. First, Space Invasion implies the intrusive modification through MAR of sensitive spaces, e.g., hospitals, memorials, etc. Then, Space Affectation means the degradation of users’ experience via interaction with undesirable MAR or malicious entities. Finally, MAR-Apps mishandling sensitive data leads to Privacy Leaks. SpaceMediator, a proof-of-concept MAR-App that imitates the well-known and successful MAR-App Pokémon GO, implements the solution approach of a Policy-Governed MAR-App, which assists in preventing the aforementioned mentioned security issues. Furthermore, its feasibility is evaluated through a user study with 40 participants. As a result, uncovering understandability over the security issues as participants recognized and prevented them with success rates as high as 92.50%. Furthermore, there is an enriched interest in Policy-Governed MAR-Apps as 87.50% of participants agreed with restricted MAR-Apps within sensitive spaces, and 82.50% would implement constraints in MAR-Apps. These promising results encourage adopting the Policy-Governed solution approach in future MAR-Apps.
ContributorsClaramunt, Luis Manuel (Author) / Ahn, Gail-Joon (Thesis advisor) / Rubio-Medrano, Carlos E (Committee member) / Baek, Jaejong (Committee member) / Arizona State University (Publisher)
Created2022
Description
Studies on underground forums can significantly advance the understanding of cybercrime workflow and underground economies. However, research on underground forums has concentrated on public information with little attention paid to users’ private interactions. Since detailed information will be discussed privately, the failure to investigate private interactions may miss critical intelligence and even misunderstand the entire underground economy. Furthermore, underground forums have evolved into criminal freelance markets where criminals trade illicit products and cybercrime services, allowing unsophisticated people to launch sophisticated cyber attacks. However, current research rarely examines and explores how criminals interact with each other, which makes researchers miss the opportunities to detect new cybercrime patterns proactively. Moreover, in clearnet, criminals are active in exploiting human vulnerabilities to conduct various attacks, and the phishing attack is one of the most prevalent types of cybercrime. Phishing awareness training has been proven to decrease the rate of clicking phishing emails. However, the rate of reporting phishing attacks is unexpectedly low based on recent studies, leaving phishing websites with hours of additional active time before being detected. In this dissertation, I first present an analysis of private interactions in underground forums and introduce machine learning-based approaches to detect hidden connections between users. Secondly, I analyze how criminals collaborate with each other in an emerging scam service in underground forums that exploits the return policies of merchants to get a refund or a replacement without returning the purchased products. Finally, I conduct a comprehensive evaluation of the phishing reporting ecosystem to identify the critical challenges while reporting phishing attacks to enable people to fight against phishers proactively.
ContributorsSun, Zhibo (Author) / Ahn, Gail-Joon (Thesis advisor) / Doupe, Adam (Thesis advisor) / Bao, Tiffany (Committee member) / Benjamin, Victor (Committee member) / Arizona State University (Publisher)
Created2022
Description
Realistic lighting is important to improve immersion and make mixed reality applications seem more plausible. To properly blend the AR objects in the real scene, it is important to study the lighting of the environment. The existing illuminationframeworks proposed by Google’s ARCore (Google’s Augmented Reality Software Development Kit) and Apple’s ARKit (Apple’s Augmented Reality Software Development Kit) are computationally expensive and have very slow refresh rates, which make them incompatible for dynamic environments and low-end mobile devices. Recently, there have been other illumination estimation frameworks such as GLEAM, Xihe, which aim at providing better illumination with faster refresh rates. GLEAM is an illumination estimation framework that understands the real scene by collecting pixel data from a reflecting spherical light probe. GLEAM uses this data to form environment cubemaps which are later mapped onto a reflection probe to generate illumination for AR objects.
It is noticed that from a single viewpoint only one half of the light probe can be observed at a time which does not give complete information about the environment. This leads to the idea of having a multi-viewpoint estimation for better performance. This thesis work analyzes the multi-viewpoint capabilities of AR illumination frameworks that use physical light probes to understand the environment. The current work builds networking using TCP and UDP protocols on GLEAM. This thesis work also documents how processor load sharing has been done while networking devices and how that benefits the performance of GLEAM on mobile devices. Some enhancements using multi-threading have also been made to the already existing GLEAM model to improve its performance.
ContributorsGurram, Sahithi (Author) / LiKamWa, Robert (Thesis advisor) / Jayasuriya, Suren (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2022
Description
In some scenarios, true temporal ordering is required to identify the actions occurring in a video. Recently a new synthetic dataset named CATER, was introduced containing 3D objects like sphere, cone, cylinder etc. which undergo simple movements such as slide, pick & place etc. The task defined in the dataset is to identify compositional actions with temporal ordering. In this thesis, a rule-based system and a window-based technique are proposed to identify individual actions (atomic) and multiple actions with temporal ordering (composite) on the CATER dataset. The rule-based system proposed here is a heuristic algorithm that evaluates the magnitude and direction of object movement across frames to determine the atomic action temporal windows and uses these windows to predict the composite actions in the videos. The performance of the rule-based system is validated using the frame-level object coordinates provided in the dataset and it outperforms the performance of the baseline models on the CATER dataset. A window-based training technique is proposed for identifying composite actions in the videos. A pre-trained deep neural network (I3D model) is used as a base network for action recognition. During inference, non-overlapping windows are passed through the I3D network to obtain the atomic action predictions and the predictions are passed through a rule-based system to determine the composite actions. The approach outperforms the state-of-the-art composite action recognition models by 13.37% (mAP 66.47% vs. mAP 53.1%).
ContributorsMaskara, Vivek Kumar (Author) / Venkateswara, Hemanth (Thesis advisor) / McDaniel, Troy (Thesis advisor) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2022
Description
In this work, I propose a novel, unsupervised framework titled SATLAB, to label satellite images, given a classification task at hand. Existing models for satellite image classification such as DeepSAT and DeepSAT-V2 rely on deep learning models that are label-hungry and require a significant amount of training data. Since manual curation of labels is expensive, I ensure that SATLAB requires zero training labels. SATLAB can work in conjunction with several generative and unsupervised machine learning models by allowing them to be seamlessly plugged into its architecture. I devise three operating modes for SATLAB - manual, semi-automatic and automatic which require varying levels of human intervention in creating the domain-specific labeling functions for each image that can be utilized by the candidate generative models such as Snorkel, as well as other unsupervised learners in SATLAB. Unlike existing supervised learning baselines which only extract textural features from satellite images, I support the extraction of both textural and geospatial features in SATLAB, and I empirically show that geospatial features enhance the classification F1-score by 33%. I build SATLAB on the top of Apache Sedona in order to leverage its rich set of spatial query processing operators for the extraction of geospatial features from satellite raster images. I evaluate SATLAB on a target binary classification task that distinguishes slum from non-slum areas, upon a repository of 100K satellite images captured by the Sentinel satellite program. My 5-Fold Cross Validation (CV) experiments show that SATLAB achieves competitive F1-scores (0.6) using 0% labeled data while the best baseline supervised learning baseline achieves 0.74 F1-score using 80% labeled data. I also show that Snorkel outperforms alternative generative and unsupervised candidate models that can be plugged into SATLAB by 33% to 71% w.r.t. F1-score and 3 times to 73 times w.r.t. latency. I also show that downstream classifiers trained using the labels generated by SATLAB are comparable in quality (0.63 F1) to their counterpart classifiers (0.74 F1) trained on manually curated labels.
ContributorsAggarwal, Shantanu (Author) / Sarwat, Mohamed (Thesis advisor) / Zou, Jia (Committee member) / Boscovic, Dragan (Committee member) / Arizona State University (Publisher)
Created2022