Physical Sciences and Mathematics Commons^™

Codeval, Aditi Agrawal

Master's Projects

Grading coding assignments call for a lot of work. There are numerous aspects of the code that need to be checked, such as compilation errors, runtime errors, the number of test cases passed or failed, and plagiarism. Automated grading tools for programming assignments can be used to help instructors and graders in evaluating the programming assignments quickly and easily. Creating the assignment on Canvas is again a time taking process and can be automated. We developed CodEval, which instantly grades the student assignment submitted on Canvas and provides feedback to the students. It also uploads, creates, and edits assignments, thereby …

Image Captioning Using Reinforcement Learning, Venkat Teja Golamaru

Master's Projects

Image captioning is a crucial technology with numerous applications, including enhancing accessibility for the visually impaired, developing automated image indexing and retrieval systems, and enriching social media experiences. However, accurately describing the content of an image in natural language remains a challenge, particularly in low-resource settings where data and computational power are limited. The most advanced image captioning architectures currently use encoder-decoder structures that incorporate a sequential recurrent prediction model. This study adopts a typical Convolutional Neural Network (CNN) encoder Recurrent Neural Network (RNN) decoder structure for image captioning, but it has framed the problem as a sequential decision-making task. …

Relationalnet Using Graph Neural Networks For Social Recommendations, Dharahas Tallapally

Master's Projects

Traditional recommender systems create models that can predict user interests based on the user-item relationships. However, these systems often have limited performance due to sparse user behavior data. To address this challenge, researchers are now exploring models for social recommendation that can account for both user- user and user-item relationships based on social networks, and user past behavior, respectively. These models aim to understand each user’s behavior by considering their trusted neighbors and their influence on each other. Specifically, the potential embedding of each user is influenced by their trusted neighbors, who are, in turn, influenced by their own trusted …

Resource Coordination Learning For End-To-End Network Slicing Under Limited State Visibility, Xiang Liu

Master's Projects

This paper discusses a resource coordination problem under limited state visibility to realize end-to-end network slices that are hosted by multiple network domains. We formulate this resource coordination problem as a special type of the multi- armed bandit (MAB) problem called the combinatorial multi-armed bandit (CMAB) problem. Based on this formulation, we convert the problem to a regret minimization problem with a linear objective function and solve it by adapting the Learning with Linear Rewards (LLR) algorithm. In this paper, we present a new hybrid approach that incorporates state reports, which include partial resource information in each domain, into the …

Airport Assignment For Emergency Aircraft Using Reinforcement Learning, Saketh Kamatham

Master's Projects

The volume of air traffic is increasing exponentially every day. The Air Traffic Control (ATC) at the airport has to handle aircraft runway assignments for landing and takeoff and airspace maintenance by directing passing aircraft through the airspace safely. If any aircraft is facing a technical issue or problem and is in a state of emergency, it requires expedited landing to respond to that emergency. The ATC gives this aircraft priority to landing and assistance. This process is very strenuous as the ATC has to deal with multiple aspects along with the emergency aircraft. It is the duty of the …

A Novel Efficient Deep Learning Framework For Facial Inpainting - Face Reconstruction From Masked Images, Akshay Ravi

Master's Projects

The use of face masks due to the covid-19 pandemic has made surveillance of people very difficult. Since a mask covers most of the facial components, security cameras are rendered of little to no use in the identification of criminals. In order to realize what a face looks like behind a mask, we have to construct the facial features in the masked region. On a higher level, this falls under the field of image inpainting, i.e. filling missing regions of images or correcting irregularities in images. Current research on image inpainting shows promising results on images that have missing/incorrect patches …

Multi-Label Text Classification With Transfer Learning, Likhitha Yelamanchili

Master's Projects

Multi-label text categorization is a crucial task in Natural Language Processing, where each text instance can be simultaneously assigned to numerous labels. This project's goal is to assess how well several deep learning models perform on a real-world dataset for multi-label text classification. We employed data augmentation techniques like Synonym Substitution and Random Word Substitution to address the problem of data imbalance. We conducted experiments on a toxic comment classification dataset to evaluate the effectiveness of several deep learning models including Bi-LSTM, GRU, and Bi-GRU, as well as fine- tuned pre-trained BERT models. Many metrics, including log loss, recall@k, and …

Context Aware Neural Machine Translation Using Graph Encoders, Saurabh Kale

Master's Projects

Machine translation presents its root in the domain of textual processing that focuses on the usage of computer software for the purpose of translation of sentences. Neural machine translation follows the same idea and integrates machine learning with the help of neural networks.Various techniques are being explored by researchers and are famously used by Google Translate, Bing Microsoft Translator, Deep Translator, etc. However, these neural machine translation techniques do not incorporate the context of the sentences and are only determined by the phrasesor sentence structure. This report explores the neural machine translation technique dedicated to context-aware translations. It also provides …

Rideshare Using Degrees Of Separation: A Social Network-Based Approach, Gokul Garikipati

Master's Projects

Conventional ride-sharing services, such as Lyft and Uber, routinely match drivers with riders based on their proximity to each other, using GPS coordinates and mapping technology. The application then calculates the cost of the ride based on factors such as distance traveled and time spent in the car. The concept of six degrees of separation suggests that a maximum of 6 steps or relationships can connect any two individuals in the world. This idea could be applied to a ride-share service to provide a more personalized and efficient experience for users. Instead of just matching riders with drivers based on …

Comparative Analysis Of Transformer-Based Models For Text-To-Speech Normalization, Pankti Dholakia

Master's Projects

Text-to-Speech (TTS) normalization is an essential component of natural language processing (NLP) that plays a crucial role in the production of natural-sounding synthesized speech. However, there are limitations to the TTS normalization procedure. Lengthy input sequences and variations in spoken language can present difficulties. The motivation behind this research is to address the challenges associated with TTS normalization by evaluating and comparing the performance of various models. The aim is to determine their effectiveness in handling language variations. The models include LSTM-GRU, Transformer, GCN-Transformer, GCNN-Transformer, Reformer, and a BERT language model that has been pre-trained. The research evaluates the performance …

Sign Language Recognition Using A Hybrid Machine Learning Model, Peeyusha Shivayogi

Master's Projects

Sign Language is a visual language used by millions of people around the world. American Sign Language (ASL) is one of the most popular sign languages and the third most popular language in the United States. Automatic recognition of ASL signs can help bridge the communication gap between deaf and hearing individuals. In this project, we explore the use of deep learning models for ASL sign recognition, using the MNIST dataset as a benchmark. We preprocessed the data by reshaping the images to the input layer size of the models and normalized the pixel values. We evaluated five popular deep-learning …

The New Student: The Enhancement Of An Ebook To Support Emotional Connection, Rebecca Zumaeta

Master's Projects

EBooks are a form of multimedia applications that encourage cognitive learning. Multimedia can also influence readers to have a deeper connection to the story. Understanding the influence of a static picture book versus an animated and audio guided eBook can prove valuable in developing learning media and other forms of content. In this research we take a published children's book and apply the content into a multimedia eBook. The purpose of the creation of the eBook is to compare the interest of a reader on a story when static format, when some multimedia is added and when the story is …

Bias Detector Tool For Face Datasets Using Image Recognition, Jatin Vamshi Battu

Master's Projects

Computer Vision has been quickly transforming the way we live and work. One of its sub- domains, i.e., Facial Recognition has also been advancing at a rapid pace. However, the development of machine learning models that power these systems has been marred by social biases, which open the door to various societal issues. The objective of this project is to address these issues and ensure that computer vision systems are unbiased and fair to all individuals. To achieve this, we have created a web tool that uses three image classifiers (implemented using CNNs) to classify images into categories based on …

High Performance Distributed File System Based On Blockchain, Ajinkya Rajguru

Master's Projects

Distributed filesystem architectures use commodity hardware to store data on a large scale with maximum consistency and availability. Blockchain makes it possible to store information that can never be tampered with and incentivizes a traditional decentralized storage system. This project aimed to implement a decentralized filesystem that leverages the blockchain to keep a record of all the transactions on it. A conventional filesystem viz. GFS [1] or HDFS [2] uses designated servers owned by their organization to store the data and are governed by a master service. This project aimed at removing a single point of failure and makes use …

Navigating Classic Atari Games With Deep Learning, Ayan Abhiranya Singh

Master's Projects

Games for the Atari 2600 console provide great environments for testing reinforcement learning algorithms. In reinforcement learning algorithms, an agent typically learns about its environment via the delivery of periodic rewards. Deep Q-Learning, a variant of Q-Learning, utilizes neural networks which train a Q-function to predict the highest future reward given an input state and action. Deep Q-learning has shown great results in training agents to play Atari 2600 games like Space Invaders and Breakout. However, Deep Q-Learning has historically struggled with learning how to play games with greater emphasis on exploration and delayed rewards, like Ms. PacMan. In this …

Robust Cache System For Web Search Engine Yioop, Rushikesh Padia

Master's Projects

Caches are the most effective mechanism utilized by web search engines to optimize the performance of search queries. Search engines employ caching at multiple levels to improve its performance, for example, caching posting list and caching result set. Caching query results reduces overhead of processing frequent queries and thus saves a lot of time and computing power. Yioop is an open-source web search engine which utilizes result cache to optimize searches. The current implementation utilizes a single dynamic cache based on Marker’s algorithm. The goal of the project is to improve the performance of cache in Yioop. To choose a …

Document-Level Machine Translation With Hierarchical Attention, Yu-Tang Shen

Master's Projects

Machine translation (MT) aims to translate texts with minimal human involvement, and the utilization of machine learning methods is pivotal to its success. Sentence-level and paragraph-level translations were well-explored in the past decade, such as the Transformer and its variations, but less research was done on the document level. From reading a piece of news in a different language to trying to understand foreign research, document-level translation can be helpful.

This project utilizes a hierarchical attention (HAN) mechanism to abstract context information making document-level translation possible. It further utilizes the Big Bird attention mask in the hope of reducing memory …

Static Taint Analysis Via Type-Checking In Typescript, Abhijn Chadalawada

Master's Projects

With the widespread use of web applications across the globe, and the ad- vancements in web technologies in recent years, these applications have grown more ubiquitous and sophisticated than ever before. Modern web applications face the constant threat of numerous web security risks given their presence on the internet and the massive influx of data from external sources. This paper presents a novel method for analyzing taint through type-checking and applies it to web applications in the context of preventing online security threats. The taint analysis technique is implemented in TypeScript using its built-in type-checking features, and then integrated into …

Yelp Restaurant Popularity Score Calculator, Sneh Bindesh Chitalia

Master's Projects

Yelp is a popular social media platform that has gained much traction over the last few years. The critical feature of Yelp is it has information about any small or large-scale business, as well as reviews received from customers. The reviews have both a 1 to 5 star rating, as well as text. For a particular business, any user can view the reviews, but the stars are what most users check because it is an easy and fast way to decide. Therefore, the star rating is a good metric to measure a particular business’s value. However, there are other attributes …

Malware Classification Using Api Call Information And Word Embeddings, Sahil Aggarwal

Master's Projects

Malware classification is the process of classifying malware into recognizable categories and is an integral part of implementing computer security. In recent times, machine learning has emerged as one of the most suitable techniques to perform this task. Models can be trained on various malware features such as opcodes, and API calls among many others to deduce information that would be helpful in the classification.

Word embeddings are a key part of natural language processing and can be seen as a representation of text wherein similar words will have closer representations. These embeddings can be used to discover a quantifiable …

Malware Classification Using Graph Neural Networks, Manasa Mananjaya

Master's Projects

Word embeddings are widely recognized as important in natural language pro- cessing for capturing semantic relationships between words. In this study, we conduct experiments to explore the effectiveness of word embedding techniques in classifying malware. Specifically, we evaluate the performance of Graph Neural Network (GNN) applied to knowledge graphs constructed from opcode sequences of malware files. In the first set of experiments, Graph Convolution Network (GCN) is applied to knowledge graphs built with different word embedding techniques such as Bag-of-words, TF-IDF, and Word2Vec. Our results indicate that Word2Vec produces the most effective word embeddings, serving as a baseline for comparison …

Classifying World War Ii Era Ciphers With Machine Learning, Brooke Dalton

Master's Projects

We examine whether machine learning and deep learning techniques can classify World War II era ciphers when only ciphertext is provided. Among the ciphers considered are Enigma, M-209, Sigaba, Purple, and Typex. For our machine learning models, we test a variety of features including the raw ciphertext letter sequence, histograms, and n-grams. The classification is approached in two scenarios. The first scenario considers fixed plaintext encrypted with fixed keys and the second scenario considers random plaintext encrypted with fixed keys. The results show that histograms are the best feature and classic machine learning methods are more appropriate for this kind …

Concept Drift Detection In Android Malware, Inderpreet Singh

Master's Projects

Machine learning and deep learning algorithms have been successfully applied to the problems of malware detection, classification, and analysis. However, most of such studies have been limited to applying learning algorithms to a static snapshot of malware, which fails to account for concept drift, that is, the non-stationary nature of the data. In practice, models need to be updated whenever a sufficient level of concept drift has occurred. In this research, we consider concept drift detection in the context of Android malware. We train a series of Support Vector Machines (SVM) over sliding windows of time and compare the resulting …

Keystroke Dynamics And User Identification, Atharva Sharma

Master's Projects

We consider the potential of keystroke dynamics for user identification and authentication. We work with a fixed-text dataset, and focus on clustering users based on the difficulty of distinguishing their typing characteristics. After obtaining a confusion matrix, we cluster users into different levels of classification difficulty based on their typing patterns. Our goal is to create meaningful clusters that enable us to apply appropriate authentication methods to specific user clusters, resulting in an optimized balance between security and efficiency. We use a novel feature engineering method that generates image-like features from keystrokes and employ multiclass Convolutional Neural Networks (CNNs) to …

Application Of Adversarial Attacks On Malware Detection Models, Vaishnavi Nagireddy

Master's Projects

Malware detection is vital as it ensures that a computer is safe from any kind of malicious software that puts users at risk. Too many variants of these malicious software are being introduced everyday at increased speed. Thus, to guarantee security of computer systems, huge advancements in the field of malware detection are made and one such approach is to use machine learning for malware detection. Even though machine learning is very powerful, it is prone to adversarial attacks. In this project, we will try to apply adversarial attacks on malware detection models. To perform these attacks, fake samples that …

3d Ar Reconstruction, Sneh Arvind Kothari

Master's Projects

The goal of the project is to improve the shopping experience for users by using augmented reality technology. People generally want opinions from others when buying shoes offline. Clicking and sending images of a shoe is not an ideal solution as it does not give the complete feel of the shoe. We developed the 3D AR Reconstruction app to make this process better. A user of our app clicks photos of the shoe. This image data is converted to form a mesh that can be shared. On receiving a model the user can open it in the app and interact …

Enhancing The Security Of Yioop Discussion Board, Prajna Gururaj Puranik

Master's Projects

Yioop is an open-source web portal that serves as a search engine and a discussion board, enabling users to create, join, and share content within groups. Data security is a critical concern for Yioop, as it involves storing and accessing user-generated data and generating statistical data. Yioop has an existing security mechanism in place, but continuous enhancements are needed to protect against potential vulnerabilities and cyber threats.

This project aims to strengthen the security of Yioop by implementing additional security measures that build upon the existing security mechanism. To prevent statistical attacks, this project extends differential privacy to mask the …

Enhancing Facial Emotion Recognition Using Image Processing With Cnn, Sourabh Deokar

Master's Projects

Facial expression recognition (FER) has been a challenging task in computer vision for decades. With recent advancements in deep learning, convolutional neural networks (CNNs) have shown promising results in this field. However, the accuracy of FER using CNNs heavily relies on the quality of the input images and the size of the dataset. Moreover, even in pictures of the same person with the same expression, brightness, backdrop, and stance might change. These variations are emphasized when comparing pictures of individuals with varying ethnic backgrounds and facial features, which makes it challenging for deep-learning models to classify. In this paper, we …

Twitter Bot Detection Using Nlp And Graph Classification, Warada Jayant Kulkarni

Master's Projects

Social media platforms are one of the primary resources for information as it is easily accessible, low in cost, and provides a high rate of information spread. Online social media (OSM) have become the main source of news information around the world, but because of the distributed nature of the web, it has increased the risk of fake news spread. Fake news is misleading information that is published as real news. Therefore, identifying fake news and flagging them as such, as well as detecting sources that generate them is an ongoing task for researchers and OSM companies. Bots are artificial …

Driving Simulator : Driving Performance Under Distraction, Kaushik Pilligundla

Master's Projects

This pilot study used a driving simulator experiment to look into how podcast consumption affects driving performance as a continuous distraction. Three volunteers conducted three trials in the study, each with a different driving scenario. Data analysis was done to compare two conditions. The first condition is the Audio, where volunteers listen to podcasts while driving. The second condition is no-audio condition.. The no-audi condition had nothing to play in the background. We used eye-tracking technology to gather gaze data. The study's findings using the post survey and eye fixation data indicate that listening to podcasts leads to continuous distraction …