Artificial Intelligence and Robotics Commons^™

Post-Quantum Secure Identity-Based Encryption Scheme Using Random Integer Lattices For Iot-Enabled Ai Applications, Dharminder Dharminder, Ashok Kumar Das, Sourav Saha, Basudeb Bera, Athanasios V. Vasilakos

VMASC Publications

Identity-based encryption is an important cryptographic system that is employed to ensure confidentiality of a message in communication. This article presents a provably secure identity based encryption based on post quantum security assumption. The security of the proposed encryption is based on the hard problem, namely Learning with Errors on integer lattices. This construction is anonymous and produces pseudo random ciphers. Both public-key size and ciphertext-size have been reduced in the proposed encryption as compared to those for other relevant schemes without compromising the security. Next, we incorporate the constructed identity based encryption (IBE) for Internet of Things (IoT) applications, …

Gesture Recognition Using Neural Networks, Ashwini Kurady

Master's Projects

The advances in technology have brought in a lot of changes in the way humans go about their lives. This has enhanced the significance of Artificial Neural Networks and Computer Vision- based interactions with the world. Gesture Recognition is one of the major focus areas in Computer Vision. This involves Human Computer Interfaces (HCI) that would capture and understand human actions. In this project, we will explore how Neural Network concepts can be applied in this challenging field of Computer Vision. By leveraging the latest research for Gesture Recognition, we researched on how to capture the movement across different frames …

Contextualized Vector Embeddings For Malware Detection, Vinay Pandya

Master's Projects

Malware classification is a technique to classify different types of malware which form an integral part of system security. The aim of this project is to use context dependant word embeddings to classify malware. Tansformers is a novel architecture which utilizes self attention to handle long range dependencies. They are particularly effective in many complex natural language processing tasks such as Masked Lan- guage Modelling(MLM) and Next Sentence Prediction(NSP). Different transfomer architectures such as BERT, DistilBert, Albert, and Roberta are used to generate context dependant word embeddings. These embeddings would help in classifying different malware samples based on their similarity …

Predicting Externally Visible Traits From A Dna Sample For Law Enforcement Applications, Niraj Pandkar

Master's Projects

A large majority of crimes such as homicides, sexual assaults and missing person cases are not solved within a reasonable timeframe and become cold cases. The ability to predict visual appearance and ancestry from a DNA sample will provide an unprecedented advancement in such criminal investigations. DNA based prediction of craniofacial features, phenotypes and ancestry can be used to reduce the pool of candidates onto which to perform further investigations. To achieve the above goal, it is first essential to substantiate, model and measure the intrinsic relationship between the genomic markers and phenotypic features. The first step is to standardize …

Generative Adversarial Networks For Image-Based Malware Classification, Huy Nguyen

Master's Projects

Malware detection and analysis are important topics in cybersecurity. For efficient malware removal, determination of malware threat levels, and damage estimation, malware family classification plays a critical role. With the rise in computing power and the advent of cloud computing, deep learning models for malware analysis has gained in popularity. In this paper, we extract features from malware executable files and represent them as images using various approaches. We then focus on Generative Adversarial Networks (GAN) for multiclass classification and compare our GAN results to other popular machine learning techniques, including Support Vector Machine

(SVM), XGBoost, and Restricted Boltzmann Machines …

Faking Sensor Noise Information, Justin Chang

Master's Projects

Noise residue detection in digital images has recently been used as a method to classify images based on source camera model type. The meteoric rise in the popularity of using Neural Network models has also been used in conjunction with the concept of noise residuals to classify source camera models. However, many papers gloss over the details on the methods of obtaining noise residuals and instead rely on the self- learning aspect of deep neural networks to implicitly discover this themselves. For this project I propose a method of obtaining noise residuals (“noiseprints”) and denoising an image, as well as …

Adversarial Attacks On Speech Separation Systems, Kendrick Trinh

Master's Projects

Speech separation is a special form of blind source separation in which the objective is to decouple two or more sources such that they are distinct. The need for such an ability grows as speech activated device usage increases in our every day life. These systems, however, are susceptible to malicious actors. In this work, we repurpose proven adversarial attacks and leverage them against a combination speech separation and speech recognition system. The attack adds adversarial noise to a mixture of two voices such that the two outputs of the speech separation system are similarly transcribed by the speech recognition …

Robustness Of Image-Based Malware Analysis, Katrina Tran

Master's Projects

Being able to identify malware is important in preventing attacks. Image-based malware analysis is the study of images that are created from malware. Analyzing these images can help identify patterns in malware families. In previous work, "gist descriptor" features extracted from images have been used in malware classification problems and have shown promising results. In this research, we determine whether gist descriptors are robust with respect to malware obfuscation techniques, as compared to Convolutional Neural Networks (CNN) trained directly on malware images. Using the Python Image Library, we create images from malware executables and from malware that we obfuscate. We …

A Novel Handover Method Using Destination Prediction In 5g-V2x Networks, Pooja Shyamsundar

Master's Projects

This paper proposes a novel approach to handover optimization in fifth generation vehicular networks. A key principle in designing fifth generation vehicular network technology is continuous connectivity. This makes it important to ensure that there are no gaps in communication for mobile user equipment. Handovers can cause disruption in connectivity as the process involves switching from one base station to another. Issues in the handover process include poor load management for moving traffic resulting in low bandwidth or connectivity gaps, too many hops resulting in multiple unneccessary handovers, short dwell times and ineffective base station selection resulting in delays and …

Cloud Provisioning And Management With Deep Reinforcement Learning, Alexandru Tol

Master's Projects

The first web applications appeared in the early nineteen nineties. These applica- tions were entirely hosted in house by companies that developed them. In the mid 2000s the concept of a digital cloud was introduced by the then CEO of google Eric Schmidt. Now in the current day most companies will at least partially host their applications on proprietary servers hosted at data-centers or commercial clouds like Amazon Web Services (AWS) or Heroku.

This arrangement seems like a straight forward win-win for both parties, the customer gets rid of the hassle of maintaining a live server for their applications and …

Analysis Of Public Sentiment Of Covid-19 Pandemic, Vaccines, And Lockdowns, Devinesh Singh

Master's Projects

CoV-2 pandemic prompted lockdown measures to be implemented worldwide; these directives were implemented nationwide to stunt the spread of the infection. Throughout the lockdowns, millions of individuals resorted to social media for entertainment, communicate with friends and family, and express their opinions about the pandemic. Simultaneously, social media aided in the dissemination of misinformation, which has proven to be a threat to global health. Sentiment analysis, a technique used to analyze textual data, can be used to gain an overview of public opinion behind CoV-2 from Twitter and TikTok. The primary focus of the project is to build a deep …

Caption And Image Based Next-Word Auto-Completion, Meet Patel

Master's Projects

With the increasing number of options or choices in terms of entities like products, movies, songs, etc. which are now available to users, they try to save time by looking for an application or system that provides automatic recommendations. Recommender systems are automated computing processes that leverage concepts of Machine Learning, Data Mining and Artificial Intelligence towards generating product recommendations based on a user’s preferences. These systems have given a significant boost to businesses across multiple segments as a result of reduced human intervention. One similar aspect of this is content writing. It would save users a lot of time …

Multi-Step Prediction Using Tree Generation For Reinforcement Learning, Kevin Prakash

Master's Projects

The goal of reinforcement learning is to learn a policy that maximizes a reward function. In some environments with complete information, search algorithms are highly useful in simulating action sequences in a game tree. However, in many practical environments, such effective search strategies are not applicable since their state transition information may not be available. This paper proposes a novel method to approximate a game tree that enables reinforcement learning to use search strategies even in incomplete information environments. With an approximated game tree, the agent predicts all possible states multiple steps into the future and evaluates the states to …

Empirical Evaluation Of The Shift And Scale Parameters In Batch Normalization, Yashna Peerthum

Master's Projects

Batch Normalization (BatchNorm) is a technique that enables the training of deep neural networks, especially Convolutional Neural Networks (CNN) for computer vision tasks. It has been empirically demonstrated that BatchNorm increases per- formance, stability, and accuracy, although the reasons for these improvements are unclear. BatchNorm consists of a normalization step with trainable shift and scale parameters. In this paper, we examine the role of normalization and the shift and scale parameters in BatchNorm. We implement two new optimizers in PyTorch: a version of BatchNorm that we refer to as AffineLayer, which includes the shift and scale transform without normalization, and …

Using Machine Learning To Maximize First-Generation Student Success A Contribution To The Mission Of Aiding The Underserved, Mustafa Emre Yesilyurt

Master's Projects

The Leadership and Career Accelerator (UNVS 101) is a course offered at San José State University (SJSU) designed to hone industry skills in and provide support to students of underserved backgrounds. The main goal of this study is to determine which features are most significant to identifying the students at risk of failing the course. This will allow faculty to better focus data collection efforts and facilitate an increase in classifier accuracy. The data came as three distinct sets (sources). One contained features describing student demographics and academic history, another described the students’ experience in the course, and a third …

Abstractive Text Summarization For Tweets, Siyu Chen

Master's Projects

In the high-tech age, we can access a vast number of articles, information, news, and opinion online. The wealth of information allows us to learn about the topics we are interested in more easily and cheaply, but it also requires us to spend an enormous amount of time reading online. Text summarization can help us save a lot of reading time so that we can know more information in a shorter period. The primary goal of text summarization is to shorten the text while including as much vital information as possible in the original text so fewer people use this …

A Study On Human Face Expressions Using Convolutional Neural Networks And Generative Adversarial Networks, Sriramm Muthyala Sudhakar

Master's Projects

Human beings express themselves via words, signs, gestures, and facial emotions. Previous research using pre-trained convolutional models had been done by freezing the entire network and running the models without the use of any image processing techniques. In this research, we attempt to enhance the accuracy of many deep CNN architectures like ResNet and Senet, using a variety of different image processing techniques like Image Data Generator, Histogram Equalization, and UnSharpMask. We used FER 2013, which is a dataset containing multiple classes of images. While working on these models, we decided to take things to the next level, and we …

Enhancing Diagnosis Through Technology: Decision Support, Artificial Intelligence, And Beyond, Robert El-Kareh, Dean F Sittig

Faculty, Staff and Student Publications

Patient care in intensive care environments is complex, time-sensitive, and data-rich, factors that make these settings particularly well-suited to clinical decision support (CDS). A wide range of CDS interventions have been used in intensive care unit environments. The field needs well-designed studies to identify the most effective CDS approaches. Evolving artificial intelligence and machine learning models may reduce information-overload and enable teams to take better advantage of the large volume of patient data available to them. It is vital to effectively integrate new CDS into clinical workflows and to align closely with the cognitive processes of frontline clinicians.

Behavioral Predictive Analytics Towards Personalization For Self-Management – A Use Case On Linking Health-Related Social Needs, Bon Sy, Michael Wassil, Helene Connelly, Alisha Hassan

Publications and Research

The objective of this research is to investigate the feasibility of applying behavioral predictive analytics to optimize patient engagement in diabetes self-management, and to gain insights on the potential of infusing a chatbot with NLP technology for discovering health-related social needs. In the U.S., less than 25% of patients actively engage in self-health management even though self-health management has been reported to associate with improved health outcomes and reduced healthcare costs. The proposed behavioral predictive analytics relies on manifold clustering to identify subpopulations segmented by behavior readiness characteristics that exhibit non-linear properties. For each subpopulation, an individualized auto-regression model and …

A Novel Tropical Geometry-Based Interpretable Machine Learning Method: Pilot Application To Delivery Of Advanced Heart Failure Therapies, Heming Yao, Harm Derkson, Jessica R. Golbus, Justin Zhang, Keith D. Aaronson, Jonathan Gryak, Kayvan Najarian

Publications and Research

Abstract—A model’s interpretability is essential to many practical applications such as clinical decision support systems. In this paper, a novel interpretable machine learning method is presented, which can model the relationship between input variables and responses in humanly understandable rules. The method is built by applying tropical geometry to fuzzy inference systems, wherein variable encoding functions and salient rules can be discovered by supervised learning. Experiments using synthetic datasets were conducted to demonstrate the performance and capacity of the proposed algorithm in classification and rule discovery. Furthermore, we present a pilot application in identifying heart failure patients that are eligible …