Matching Items (53)
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Description

Social injustice issues are a familiar, yet very arduous topic to define. This is because they are difficult to predict and tough to understand. Injustice issues negatively affect communities because they directly violate human rights and they span a wide range of areas. For instance, injustice issues can relate to

Social injustice issues are a familiar, yet very arduous topic to define. This is because they are difficult to predict and tough to understand. Injustice issues negatively affect communities because they directly violate human rights and they span a wide range of areas. For instance, injustice issues can relate to unfair labor practices, racism, gender bias, politics etc. This leaves numerous individuals wondering how they can make sense of social injustice issues and perhaps take efforts to stop them from occurring in the future. In an attempt to understand the rather complicated nature of social injustice, this thesis takes a data driven approach to define a social injustice index for a specific country, India. The thesis is an attempt to quantify and track social injustice through social media to see the current social climate. This was accomplished by developing a web scraper to collect hate speech data from Twitter. The tweets collected were then classified by their level of hate and presented on a choropleth map of India. Ultimately, a user viewing the ‘India Social Injustice Index’ map should be able to simply view an index score for a desired state in India through a single click. This thesis hopes to make it simple for any user viewing the social injustice map to make better sense of injustice issues.

ContributorsDeosthali, Shefali (Author) / Chavez-Echeagaray, Maria Elena (Thesis director) / Mathews, Nicolle (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05
Description

The aim of this project is to understand the basic algorithmic components of the transformer deep learning architecture. At a high level, a transformer is a machine learning model based off of a recurrent neural network that adopts a self-attention mechanism, which can weigh significant parts of sequential input data

The aim of this project is to understand the basic algorithmic components of the transformer deep learning architecture. At a high level, a transformer is a machine learning model based off of a recurrent neural network that adopts a self-attention mechanism, which can weigh significant parts of sequential input data which is very useful for solving problems in natural language processing and computer vision. There are other approaches to solving these problems which have been implemented in the past (i.e., convolutional neural networks and recurrent neural networks), but these architectures introduce the issue of the vanishing gradient problem when an input becomes too long (which essentially means the network loses its memory and halts learning) and have a slow training time in general. The transformer architecture’s features enable a much better “memory” and a faster training time, which makes it a more optimal architecture in solving problems. Most of this project will be spent producing a survey that captures the current state of research on the transformer, and any background material to understand it. First, I will do a keyword search of the most well cited and up-to-date peer reviewed publications on transformers to understand them conceptually. Next, I will investigate any necessary programming frameworks that will be required to implement the architecture. I will use this to implement a simplified version of the architecture or follow an easy to use guide or tutorial in implementing the architecture. Once the programming aspect of the architecture is understood, I will then Implement a transformer based on the academic paper “Attention is All You Need”. I will then slightly tweak this model using my understanding of the architecture to improve performance. Once finished, the details (i.e., successes, failures, process and inner workings) of the implementation will be evaluated and reported, as well as the fundamental concepts surveyed. The motivation behind this project is to explore the rapidly growing area of AI algorithms, and the transformer algorithm in particular was chosen because it is a major milestone for engineering with AI and software. Since their introduction, transformers have provided a very effective way of solving natural language processing, which has allowed any related applications to succeed with high speed while maintaining accuracy. Since then, this type of model can be applied to more cutting edge natural language processing applications, such as extracting semantic information from a text description and generating an image to satisfy it.

ContributorsCereghini, Nicola (Author) / Acuna, Ruben (Thesis director) / Bansal, Ajay (Committee member) / Barrett, The Honors College (Contributor) / Software Engineering (Contributor)
Created2023-05
Description

This research investigates the attitude of students towards chatbots and their potential usage in finding career resources. Survey data from two sources were analyzed using descriptive statistics and correlation analysis. The first survey found that students had a neutral attitude towards chatbots, but chatbot understanding was a key factor in

This research investigates the attitude of students towards chatbots and their potential usage in finding career resources. Survey data from two sources were analyzed using descriptive statistics and correlation analysis. The first survey found that students had a neutral attitude towards chatbots, but chatbot understanding was a key factor in increasing their usage. The survey data suggested that chatbots could provide quick and convenient access to information and personalized recommendations, but their effectiveness for career resource searches may be limited. The second survey found that students who were more satisfied with the quality of resources from the career office were more likely to use chatbots. However, students who felt more prepared to explore their career options were less likely to use chatbots. These results suggest that the W. P. Carey Career Office could benefit from offering more and better resources to prepare students for exploring their career options and could explore the use of chatbots to enhance the quality of their resources and increase student satisfaction. Further research is needed to confirm these suggestions and explore other possible factors that may affect the use of chatbots and the satisfaction with career office resources.

ContributorsHuang, Hai (Author) / Kappes, Janelle (Thesis director) / Eaton, John (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor) / Department of Supply Chain Management (Contributor)
Created2023-05
Description
This research investigates the attitude of students towards chatbots and their potential usage in finding career resources. Survey data from two sources were analyzed using descriptive statistics and correlation analysis. The first survey found that students had a neutral attitude towards chatbots, but chatbot understanding was a key factor in

This research investigates the attitude of students towards chatbots and their potential usage in finding career resources. Survey data from two sources were analyzed using descriptive statistics and correlation analysis. The first survey found that students had a neutral attitude towards chatbots, but chatbot understanding was a key factor in increasing their usage. The survey data suggested that chatbots could provide quick and convenient access to information and personalized recommendations, but their effectiveness for career resource searches may be limited. The second survey found that students who were more satisfied with the quality of resources from the career office were more likely to use chatbots. However, students who felt more prepared to explore their career options were less likely to use chatbots. These results suggest that the W. P. Carey Career Office could benefit from offering more and better resources to prepare students for exploring their career options and could explore the use of chatbots to enhance the quality of their resources and increase student satisfaction. Further research is needed to confirm these suggestions and explore other possible factors that may affect the use of chatbots and the satisfaction with career office resources.
ContributorsHuang, Hai (Author) / Kappes, Janelle (Thesis director) / Eaton, John (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor) / Department of Supply Chain Management (Contributor)
Created2023-05
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Description
This research investigates the attitude of students towards chatbots and their potential usage in finding career resources. Survey data from two sources were analyzed using descriptive statistics and correlation analysis. The first survey found that students had a neutral attitude towards chatbots, but chatbot understanding was a key factor in

This research investigates the attitude of students towards chatbots and their potential usage in finding career resources. Survey data from two sources were analyzed using descriptive statistics and correlation analysis. The first survey found that students had a neutral attitude towards chatbots, but chatbot understanding was a key factor in increasing their usage. The survey data suggested that chatbots could provide quick and convenient access to information and personalized recommendations, but their effectiveness for career resource searches may be limited. The second survey found that students who were more satisfied with the quality of resources from the career office were more likely to use chatbots. However, students who felt more prepared to explore their career options were less likely to use chatbots. These results suggest that the W. P. Carey Career Office could benefit from offering more and better resources to prepare students for exploring their career options and could explore the use of chatbots to enhance the quality of their resources and increase student satisfaction. Further research is needed to confirm these suggestions and explore other possible factors that may affect the use of chatbots and the satisfaction with career office resources.
ContributorsHuang, Hai (Author) / Kappes, Janelle (Thesis director) / Eaton, John (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor) / Department of Supply Chain Management (Contributor)
Created2023-05
Description

On January 5, 2020, the World Health Organization (WHO) reported on the outbreak of pneumonia of unknown cause in Wuhan, China. Two weeks later, a 35-year-old Washington resident checked into a local urgent care clinic with a 4-day cough and fever. Laboratory testing would confirm this individual as the first

On January 5, 2020, the World Health Organization (WHO) reported on the outbreak of pneumonia of unknown cause in Wuhan, China. Two weeks later, a 35-year-old Washington resident checked into a local urgent care clinic with a 4-day cough and fever. Laboratory testing would confirm this individual as the first case of the novel coronavirus in the U.S., and on January 20, 2020, the Center for Disease Control (CDC) reported this case to the public. In the days and weeks to follow, Twitter, a social media platform with 450 million active monthly users as of 2020, provided many American residents the opportunity to share their thoughts on the developing pandemic online. Social media sites like Twitter are a prominent source of discourse surrounding contemporary political issues, allowing for direct communication between users in real-time. As more population centers around the world gain access to the internet, most democratic discussion, both nationally and internationally, will take place in online spaces. The activity of elected officials as private citizens in these online spaces is often overlooked. I find the ability of publics—which philosopher John Dewey defines as groups of people with shared needs—to communicate effectively and monitor the interests of political elites online to be lacking. To best align the interests of officials and citizens, and achieve transparency between publics and elected officials, we need an efficient way to measure and record these interests. Through this thesis, I found that natural language processing methods like sentiment analyses can provide an effective means of gauging the attitudes of politicians towards contemporary issues.

ContributorsHowell, Nicholas (Author) / Voorhees, Matthew (Thesis director) / Schmidt, Peter (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Life Sciences (Contributor)
Created2023-05
Description

2018, Google researchers published the BERT (Bidirectional Encoder Representations from Transformers) model, which has since served as a starting point for hundreds of NLP (Natural Language Processing) related experiments and other derivative models. BERT was trained on masked-language modelling (sentence prediction) but its capabilities extend to more common NLP tasks,

2018, Google researchers published the BERT (Bidirectional Encoder Representations from Transformers) model, which has since served as a starting point for hundreds of NLP (Natural Language Processing) related experiments and other derivative models. BERT was trained on masked-language modelling (sentence prediction) but its capabilities extend to more common NLP tasks, such as language inference and text classification. Naralytics is a company that seeks to use natural language in order to be able to categorize users who create text into multiple categories – which is a modified version of classification. However, the text that Naralytics seeks to pull from exceed the maximum token length of 512 tokens that BERT supports – so this report discusses the research towards multiple BERT derivatives that seek to address this problem – and then implements a solution that addresses the multiple concerns that are attached to this kind of model.

ContributorsNgo, Nicholas (Author) / Carter, Lynn (Thesis director) / Lee, Gyou-Re (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / Economics Program in CLAS (Contributor)
Created2023-05
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Description
In the era of information explosion and multi-modal data, information retrieval (IR) and question answering (QA) systems have become essential in daily human activities. IR systems aim to find relevant information in response to user queries, while QA systems provide concise and accurate answers to user questions. IR and

In the era of information explosion and multi-modal data, information retrieval (IR) and question answering (QA) systems have become essential in daily human activities. IR systems aim to find relevant information in response to user queries, while QA systems provide concise and accurate answers to user questions. IR and QA are two of the most crucial challenges in the realm of Artificial Intelligence (AI), with wide-ranging real-world applications such as search engines and dialogue systems. This dissertation investigates and develops novel models and training objectives to enhance current retrieval systems in textual and multi-modal contexts. Moreover, it examines QA systems, emphasizing generalization and robustness, and creates new benchmarks to promote their progress. Neural retrievers have surfaced as a viable solution, capable of surpassing the constraints of traditional term-matching search algorithms. This dissertation presents Poly-DPR, an innovative multi-vector model architecture that manages test-query, and ReViz, a comprehensive multimodal model to tackle multi-modality queries. By utilizing IR-focused pretraining tasks and producing large-scale training data, the proposed methodology substantially improves the abilities of existing neural retrievers.Concurrently, this dissertation investigates the realm of QA systems, referred to as ``readers'', by performing an exhaustive analysis of current extractive and generative readers, which results in a reliable guidance for selecting readers for downstream applications. Additionally, an original reader (Two-in-One) is designed to effectively choose the pertinent passages and sentences from a pool of candidates for multi-hop reasoning. This dissertation also acknowledges the significance of logical reasoning in real-world applications and has developed a comprehensive testbed, LogiGLUE, to further the advancement of reasoning capabilities in QA systems.
ContributorsLuo, Man (Author) / Baral, Chitta (Thesis advisor) / Yang, Yezhou (Committee member) / Blanco, Eduardo (Committee member) / Chen, Danqi (Committee member) / Arizona State University (Publisher)
Created2023
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Description
Enabling robots to physically engage with their environment in a safe and efficient manner is an essential step towards human-robot interaction. To date, robots usually operate as pre-programmed workers that blindly execute tasks in highly structured environments crafted by skilled engineers. Changing the robots’ behavior to cover new duties or

Enabling robots to physically engage with their environment in a safe and efficient manner is an essential step towards human-robot interaction. To date, robots usually operate as pre-programmed workers that blindly execute tasks in highly structured environments crafted by skilled engineers. Changing the robots’ behavior to cover new duties or handle variability is an expensive, complex, and time-consuming process. However, with the advent of more complex sensors and algorithms, overcoming these limitations becomes within reach. This work proposes innovations in artificial intelligence, language understanding, and multimodal integration to enable next-generation grasping and manipulation capabilities in autonomous robots. The underlying thesis is that multimodal observations and instructions can drastically expand the responsiveness and dexterity of robot manipulators. Natural language, in particular, can be used to enable intuitive, bidirectional communication between a human user and the machine. To this end, this work presents a system that learns context-aware robot control policies from multimodal human demonstrations. Among the main contributions presented are techniques for (a) collecting demonstrations in an efficient and intuitive fashion, (b) methods for leveraging physical contact with the environment and objects, (c) the incorporation of natural language to understand context, and (d) the generation of robust robot control policies. The presented approach and systems are evaluated in multiple grasping and manipulation settings ranging from dexterous manipulation to pick-and-place, as well as contact-rich bimanual insertion tasks. Moreover, the usability of these innovations, especially when utilizing human task demonstrations and communication interfaces, is evaluated in several human-subject studies.
ContributorsStepputtis, Simon (Author) / Ben Amor, Heni (Thesis advisor) / Baral, Chitta (Committee member) / Yang, Yezhou (Committee member) / Lee, Stefan (Committee member) / Arizona State University (Publisher)
Created2021
Description
Natural Language Processing (NLP) techniques have increasingly been used in finance, accounting, and economics research to analyze text-based information more efficiently and effectively than primarily human-centered methods. The literature is rich with computational textual analysis techniques applied to consistent annual or quarterly financial fillings, with promising results to identify similarities

Natural Language Processing (NLP) techniques have increasingly been used in finance, accounting, and economics research to analyze text-based information more efficiently and effectively than primarily human-centered methods. The literature is rich with computational textual analysis techniques applied to consistent annual or quarterly financial fillings, with promising results to identify similarities between documents and firms, in addition to further using this information in relation to other economic phenomena. Building upon the knowledge gained from previous research and extending the application of NLP methods to other categories of financial documents, this project explores financial credit contracts, better understanding the information provided through their textual data by assessing patterns and relationships between documents and firms. The main methods used throughout this project is Term Frequency-Inverse Document Frequency (to represent each document as a numerical vector), Cosine Similarity (to measure the similarity between contracts), and K-Means Clustering (to organically derive clusters of documents based on the text included in the contract itself). Using these methods, the dimensions analyzed are various grouping methodologies (external industry classifications and text derived classifications), various granularities (document-wise and firm-wise), various financial documents associated with a single firm (the relationship between credit contracts and 10-K product descriptions), and how various mean cosine similarity distributions change over time.
ContributorsLiu, Jeremy J (Author) / Wahal, Sunil (Thesis director) / Bharath, Sreedhar (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / School for the Future of Innovation in Society (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05