Filtering by
- Creators: Computer Science and Engineering Program
In the last decade, California’s imprisoned population of women has increased by nearly 400% (Chesney-Lind, 2012). The focus of this thesis is to discuss the treatment—or lack thereof—of women within California’s criminal justice system and sentencing laws. By exploring its historical approach to two criminal actions related to women, the Three Strikes law (including non-violent drug crimes) and the absence of laws accounting for experiences of female victims of domestic violence who killed their abusers, I explore how California’s criminal code has marginalized women, and present a summary of the adverse effects brought about by the gender invisibility that is endemic within sentencing policies and practice. I also discuss recent attempted and successful reforms related to these issues, which evidence a shift toward social dialogue on sentencing aiming to address gender inequity in the sentencing code. These reforms were the result of activism; organizations, academics and individuals successfully raised awareness regarding excessive and undue sentencing of women and compelled action by the legislature.
By method of a feminist analysis of these histories, I explore these two pertinent issues in California; both are related to women who, under harsh sentencing laws, were incarcerated under the state’s male-focused legislation. Responses to the inequalities found in these laws included attempts toward both visibility for women and reform related to sentencing. I analyze the ontology of sentencing reform as it relates to activism in order to discuss the implications of further criminal code legislation, as well as the implications of the 2012 reforms in practice. Through the paper, I focus upon how women have become a target of arrest and long sentences not because they are strategically arrested to equalize their representation behind bars, but because the “tough on crime” framework in the criminal code cast a wide and fixed net that incarcerated increasingly more women following the codification of both mandatory minimums and a male-oriented approach to sentencing (Chesney-Lind et. al, 2012).
Multicellular organisms consist of cells of many different types that are established during development. Each type of cell is characterized by the unique combination of expressed gene products as a result of spatiotemporal gene regulation. Currently, a fundamental challenge in regulatory biology is to elucidate the gene expression controls that generate the complex body plans during development. Recent advances in high-throughput biotechnologies have generated spatiotemporal expression patterns for thousands of genes in the model organism fruit fly Drosophila melanogaster. Existing qualitative methods enhanced by a quantitative analysis based on computational tools we present in this paper would provide promising ways for addressing key scientific questions.
Results
We develop a set of computational methods and open source tools for identifying co-expressed embryonic domains and the associated genes simultaneously. To map the expression patterns of many genes into the same coordinate space and account for the embryonic shape variations, we develop a mesh generation method to deform a meshed generic ellipse to each individual embryo. We then develop a co-clustering formulation to cluster the genes and the mesh elements, thereby identifying co-expressed embryonic domains and the associated genes simultaneously. Experimental results indicate that the gene and mesh co-clusters can be correlated to key developmental events during the stages of embryogenesis we study. The open source software tool has been made available at http://compbio.cs.odu.edu/fly/.
Conclusions
Our mesh generation and machine learning methods and tools improve upon the flexibility, ease-of-use and accuracy of existing methods.
Drosophila melanogaster has been established as a model organism for investigating the developmental gene interactions. The spatio-temporal gene expression patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these expression images will provide biological insights into the gene functions, interactions, and networks. To facilitate pattern recognition and comparison, many web-based resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison.
Results
We present a computational framework to perform anatomical keywords annotation for Drosophila gene expression images. The spatial sparse coding approach is used to represent local patches of images in comparison with the well-known bag-of-words (BoW) method. Three pooling functions including max pooling, average pooling and Sqrt (square root of mean squared statistics) pooling are employed to transform the sparse codes to image features. Based on the constructed features, we develop both an image-level scheme and a group-level scheme to tackle the key challenges in annotating Drosophila gene expression pattern images automatically. To deal with the imbalanced data distribution inherent in image annotation tasks, the undersampling method is applied together with majority vote. Results on Drosophila embryonic expression pattern images verify the efficacy of our approach.
Conclusion
In our experiment, the three pooling functions perform comparably well in feature dimension reduction. The undersampling with majority vote is shown to be effective in tackling the problem of imbalanced data. Moreover, combining sparse coding and image-level scheme leads to consistent performance improvement in keywords annotation.
Affective computing allows computers to monitor and influence people’s affects, in other words emotions. Currently, there is a lot of research exploring what can be done with this technology. There are many fields, such as education, healthcare, and marketing, that this technology can transform. However, it is important to question what should be done. There are unique ethical considerations in regards to affective computing that haven't been explored. The purpose of this study is to understand the user’s perspective of affective computing in regards to the Association of Computing Machinery (ACM) Code of Ethics, to ultimately start developing a better understanding of these ethical concerns. For this study, participants were required to watch three different videos and answer a questionnaire, all while wearing an Emotiv EPOC+ EEG headset that measures their emotions. Using the information gathered, the study explores the ethics of affective computing through the user’s perspective.
Cryptojacking is a process in which a program utilizes a user’s CPU to mine cryptocurrencies unknown to the user. Since cryptojacking is a relatively new problem and its impact is still limited, very little has been done to combat it. Multiple studies have been conducted where a cryptojacking detection system is implemented, but none of these systems have truly solved the problem. This thesis surveys existing studies and provides a classification and evaluation of each detection system with the aim of determining their pros and cons. The result of the evaluation indicates that it might be possible to bypass detection of existing systems by modifying the cryptojacking code. In addition to this classification, I developed an automatic code instrumentation program that replaces specific instructions with functionally similar sequences as a way to show how easy it is to implement simple obfuscation to bypass detection by existing systems.
This paper is centered on the use of generative adversarial networks (GANs) to convert or generate RGB images from grayscale ones. The primary goal is to create sensible and colorful versions of a set of grayscale images by training a discriminator to recognize failed or generated images and training a generator to attempt to satisfy the discriminator. The network design is described in further detail below; however there are several potential issues that arise including the averaging of a color for certain images such that small details in an image are not assigned unique colors leading to a neutral blend. We attempt to mitigate this issue as much as possible.
This paper is centered on the use of generative adversarial networks (GANs) to convert or generate RGB images from grayscale ones. The primary goal is to create sensible and colorful versions of a set of grayscale images by training a discriminator to recognize failed or generated images and training a generator to attempt to satisfy the discriminator. The network design is described in further detail below; however there are several potential issues that arise including the averaging of a color for certain images such that small details in an image are not assigned unique colors leading to a neutral blend. We attempt to mitigate this issue as much as possible.
The NCAA is changing the current rules and regulations around a student-athlete’s name, image, and likeness. Previously, student-athletes were not allowed to participate in business activities or noninstitutional promotional activities. With the new rule changes, student-athletes will be able to engage in business activities related to their own name, image, and likeness. The goal of the team was to help “prepare athletes to understand and properly navigate the evolving restrictions and guidelines around athlete name, image, and likeness”. In order to accomplish this, the team had to understand the problems student-athletes face with these changing rules and regulations. The team conducted basic market research to identify the problem. The problem discovered was the lack of communication between student-athletes and businesses. In order to verify this problem, the team conducted several interviews with Arizona State University Athletic Department personnel. From the interviews, the team identified that the user is the student-athletes and the buyer is the brands and businesses. Once the problem was verified and the user and buyer were identified, a solution that would best fit the customers was formulated. The solution is a platform that assists student-athletes navigate the changing rules of the NCAA by providing access to a marketplace optimized to working with student-athletes and offering an ease of maintaining relationships between student-athletes and businesses. The solution was validated through meetings with interested brands. The team used the business model and market potential to pitch the business idea to the brands. Finally, the team gained traction by initiating company partnerships.
Digital learning tools have become ubiquitous in virtual and in person classrooms as teachers found creative ways to engage students during the COVID 19 pandemic. Even before the pandemic and widespread remote learning, however, digital learning tools were increasingly common and a typical part of many classrooms. While all digital learning tools are worthy of study, math digital learning tools (MDLTs) designed for K - 8th grade in particular raise questions of efficacy and usefulness for classrooms. This paper shows that MDLTs are an effective tool to raise students’ math achievement across K - 8th grade, and that time spent on MDLTs can lead to better understanding of a topic than traditional, teacher led instruction. However, if the MDLT is being delivered in a language the student is not familiar with, that student will not be able to benefit from MDLTs in the way other students do. This is also true of students who receive Special Education services. Additionally, higher quality MDLTs that provide feedback that attaches meaning to students’ work creates a better learning environment for students than one with simpler feedback. Based on my experiences with student teaching this year and using the popular MDLT IXL frequently, I recommend that MDLTs not just be used for independent practice time, but for whole class, problem solving sessions where students have to use mathematical thinking in new content areas. This will build deeper conceptual learning and a greater sense of achievement in students.