Master of Computing and Information Systems, Youngstown State University, 2024, Department of Computer Science and Information Systems

The field of coding education is rapidly evolving, with emerging technologies playing a pivotal role in transforming traditional learning methodologies. This thesis introduces Dr. Lego, an innovative framework designed to revolutionize the assessment and understanding of block-based coding through the integration of sophisticated deep learning models. Dr. Lego combines cutting-edge technologies such as MobileNetV3 (Howard, 2019), for visual recognition and BERT (Devlin et al., 2018), and XLNet (Yang et al., 2019) for natural language processing to offer a comprehensive approach to evaluating coding proficiency. The research methodology involves the meticulous curation of a diverse dataset comprising projects from the LEGO SPIKE app (LEGO Education, 2022), ensuring that the models are subjected to a broad range of coding scenarios. Leveraging the dynamic educational environment provided by the LEGO SPIKE app (LEGO Education, 2022), Dr. Lego empowers users to design and implement various coding projects, fostering hands-on learning experiences. This thesis delves into methodologies aimed at enhancing coding education by exploring model integration, data generation, and fine-tuning of pre-trained models. Dr. Lego not only evaluates coding proficiency but also provides cohesive and insightful feedback, enhancing the learning experience for users. The adaptability of the framework highlights its potential to shape the future of coding education, paving the way for a new era of interactive and engaging learning experiences.

Committee: Abdu Arslanyilmaz PhD (Advisor); Feng Yu PhD (Committee Member); Carrie Jackson EdD, BCBA (Committee Member) Subjects: Computer Science; Engineering; Information Systems; Robotics; Teaching

Master of Science, University of Toledo, 2023, Cyber Security

With the rapid increase of social network spam, it's essential to empower users with the tools to detect the harmful spam effectively. However, existing tools cannot meet the requirements. In this paper, we propose and develop a live detection tool that can detect ham and spam text and images from social networks, this tool will be trained on user collected data (Image and Text) using different classifiers, where text and images are pre-processed and then passed onto the classifier that the user can choose, the user is then able to save the model and load it whenever they want to use a social network, where this tool will show the user a notification alerting them whether the post they are looking at is spam or ham before they even get the chance to read the text or look at the image, thus protecting them from clicking on malicious links that might harm their computer and steal their data. Evaluation results have demonstrated the effectiveness of our tool.

Committee: Weiqing Sun (Committee Chair); Hong Wang (Committee Member); Ahmad Javaid (Committee Member) Subjects: Computer Science

Master of Science, The Ohio State University, 2022, Computer Science and Engineering

Natural Language Processing techniques are being widely used in the industry these days to solve a variety of business problems. In this work, we experiment with the application of NLP techniques for the use case of understanding the call interactions between customers and customer service representatives and to extract interesting insights from these conversations. We focus our methodologies on understanding call transcripts of these interactions which fall under the category of long document understanding. Existing works in text encoding typically address short form text encoding. Deep Learning models like Vanilla Transformer, BERT and DistilBERT have achieved state of the art performance on a variety of tasks involving short form text but perform poorly on long documents. To address this issue, modifications to the Transformer model have been released in the form of Longformer and BigBird. However, all these models require heavy computational resources which are often unavailable in small scale companies that run on budget constraints. To address these concerns, we survey a variety of explainable and light weight text encoders that can be trained easily in a resource constrained environment. We also propose Hierarchical Self Attention based models that outperform DistilBERT, Doc2Vec and single layer self-attention networks for downstream tasks like text classification. The proposed architecture has been put into production at the local industry organization that sponsored the research (SafeAuto Inc.) and helps the company to monitor the performance of its customer service representatives.

Committee: Eric Fosler-Lussier (Committee Chair); Rajiv Ramnath (Advisor) Subjects: Artificial Intelligence; Computer Science

Master of Computer and Information Science, Cleveland State University, 2022, Washkewicz College of Engineering

Machine learning is a very useful tool to solve issues in multiple domains such as sentiment analysis, fake news detection, facial recognition, and cyberbullying. In this work, we have leveraged its ability to understand the nuances of natural language to detect cyberbullying. We have further utilized it to detect the subject of cyberbullying such as age, gender, ethnicity, and religion. Further, we have built another layer to detect the cases of misogyny in cyberbullying. In one of our experiments, we created a three-layered architecture to detect cyberbullying , then to detect if it is gender based and finally if it is a case of misogyny or not. In each of our experimentation we trained models with support vector machines, RNNLSTM, BERT and distilBERT, and evaluated it using multiple performance measuring parameters like accuracy, bias, mean square error, recall, precision and F1 score to evaluate each model more efficiently in terms of bias and fairness. In addition to fully supervised learning, we also used weakly supervised learning techniques to detect the cyberbullying and its subject during our experimentations. Finally, we compared the performance of models trained using fully supervised learning and weakly supervised learning algorithms. This comparison further demonstrated that using weak supervision we can develop models to handle complex use cases such as cyberbullying. Finally, the thesis document concludes by describing lessons learned, future work recommendations and the concluding remarks.

Committee: Sathish Kumar, Ph.D. (Committee Chair); Hongkai Yu, Ph.D. (Committee Member); Chansu Yu, Ph.D. (Committee Member) Subjects: Computer Science

MS, University of Cincinnati, 2022, Engineering and Applied Science: Computer Science

Google's BERT (Bidirectional Encoder Representations from Transformers) algorithm is a neural network based method for processing natural language. In this exploratory study we have used API call sequences to generate a language for use with BERT to perform malware classification with Support Vector Machines. Detecting malware using sequences of API calls has been shown to be a promising area for malware detection, especially when used in conjunction with other features such as opcodes and system calls. The increase in detection accuracy and efficiency achieved through the use of BERT is a desired outcome as malware authors develop more sophisticated techniques for obfuscating their behavior. We have used an open-source dataset that contains sequences of API calls from both known malware and from non-malware and have performed analysis using Support Vector Machines (SVM) for classification, a common method used in previous work on detecting malicious API-based attacks, while using BERT as a preprocessor.

Committee: Carla Purdy Ph.D. (Committee Member); Anca Ralescu Ph.D. (Committee Member); John Gallagher Ph.D. (Committee Member) Subjects: Computer Science