Artificial Intelligence and Robotics Commons^™

Engaging Drivers Via Competition: A Case Study With Arena, Hao Cheng, Shuyu Wei, Lingyu Zhang, Zimu Zhou, Yongxin. Tong

Research Collection School Of Computing and Information Systems

Sustained work enthusiasms of drivers are crucial for the success of large-scale ride-hailing platforms. In this paper, we conduct the first-of-its-kind exploration to encourage active participation of drivers via competition. We design Arena, a competition where drivers compete for prizes via completing more trips. Through a pilot study covering over 2,600 participants, we uncover the easy-win problem, an overlooked and serious issue in competition design for real-world drivers. It refers to situations where one competitor does not show up during competition whereas the other easily wins. To solve the easy-win problem without impairing motivation of drivers, we devise a novel …

Sequence-To-Sequence Learning For Automated Software Artifact Generation, Zhongxin Liu, Xin Xia, David Lo

Research Collection School Of Computing and Information Systems

During the development and maintenance of a software system, developers produce many digital artifacts besides source code, e.g., requirement documents, code comments, change history, bug reports, etc. Such artifacts are valuable for developers to understand and maintain the software system. However, creating software artifacts can be burdensome and developers sometimes neglect to write and maintain important artifacts. This problem can be alleviated by software artifact generation tools, which can assist developers in creating software artifacts and automatically generate artifacts to replace existing empty ones. The focus of this chapter is automated software artifact generation (hereon, SAG) using seq2seq learning. This …

A Hybrid Stochastic-Deterministic Minibatch Proximal Gradient Method For Efficient Optimization And Generalization, Pan Zhou, Xiao-Tong Yuan, Lin Zhouchen, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Despite the success of stochastic variance-reduced gradient (SVRG) algorithms in solving large-scale problems, their stochastic gradient complexity often scales linearly with data size and is expensive for huge data. Accordingly, we propose a hybrid stochastic-deterministic minibatch proximal gradient (HSDMPG) algorithm for strongly convex problems with linear prediction structure, e.g. least squares and logistic/softmax regression. HSDMPG enjoys improved computational complexity that is data-size-independent for large-scale problems. It iteratively samples an evolving minibatch of individual losses to estimate the original problem, and can efficiently minimize the sampled subproblems. For strongly convex loss of n components, HSDMPG attains an -optimization-error within O κ …

Why Do Robots Have Smiley Faces?, Mark Findlay

Research Collection Yong Pung How School Of Law

The author discussed why engineers and designers provide machines with the semblance of friendliness, and why it takes more than that for humans to trust AI. The ground-breaking AI in community research and policy initiative by CAIDG, supported by the National Research Foundation Singapore under its Emerging Areas Research Projects Funding Initiative, seeks to understand how and why trust can be established when humans and machines come together.

Teaching Law And Artificial Intelligence, Brendan Johnson, Francis X. Shen

Minnesota Journal of Law, Science & Technology

No abstract provided.

Soarnet, Deep Learning Thermal Detection For Free Flight, Jake T. Tallman

Master's Theses

Thermals are regions of rising hot air formed on the ground through the warming of the surface by the sun. Thermals are commonly used by birds and glider pilots to extend flight duration, increase cross-country distance, and conserve energy. This kind of powerless flight using natural sources of lift is called soaring. Once a thermal is encountered, the pilot flies in circles to keep within the thermal, so gaining altitude before flying off to the next thermal and towards the destination. A single thermal can net a pilot thousands of feet of elevation gain, however estimating thermal locations is not …

Learning Contextual Causality Between Daily Events From Time-Consecutive Images, Hongming Zhang, Yintong Huo, Xinran Zhao, Yangqiu Song, Dan Roth

Research Collection School Of Computing and Information Systems

Conventional textual-based causal knowledge acquisition methods typically require laborious and expensive human annotations. As a result, their scale is often limited. Moreover, as no context is provided during the annotation, the resulting causal knowledge records (e.g., ConceptNet) typically do not consider the context. In this paper, we move out of the textual domain to explore a more scalable way of acquiring causal knowledge and investigate the possibility of learning contextual causality from the visual signal. Specifically, we first propose a high-quality dataset Vis-Causal and then conduct experiments to demonstrate that with good language and visual representations, it is possible to …

Deep Learning On Image Forensics And Anti-Forensics, Zhangyi Shen

Dissertations

Image forensics protect the authenticity and integrity of digital images. On the contrary, as the countermeasures of digital forensics, anti-forensics is applied to expose the vulnerability of forensics tools. Consequently, forensics researchers could develop forensics tools against possible new attacks. This dissertation investigation demonstrates two image forensics methods based on convolutional neural network (CNN) and two image anti-forensics methods based on generative adversarial network (GAN).

Detecting unsharp masking (USM) sharpened image is the first study in this dissertation. A CNN architecture comprises four convolutional layers and a classification module is proposed to discriminate sharpened images and unsharpened images. The results …

Convolutional Neural Networks For Deflate Data Encoding Classification Of High Entropy File Fragments, Nehal Ameen

LSU New Orleans Theses and Dissertations

Data reconstruction is significantly improved in terms of speed and accuracy by reliable data encoding fragment classification. To date, work on this problem has been successful with file structures of low entropy that contain sparse data, such as large tables or logs. Classifying compressed, encrypted, and random data that exhibit high entropy is an inherently difficult problem that requires more advanced classification approaches. We explore the ability of convolutional neural networks and word embeddings to classify deflate data encoding of high entropy file fragments after establishing ground truth using controlled datasets. Our model is designed to either successfully classify file …

Land Cover Image Segmentation Based On Individual Class Binary Segmentation, Sathyanarayanan Somasunder

Theses

Remote sensing techniques have been developed over the past decades to acquire data without being in contact of the target object or data source. Their application on land-cover image segmentation has attracted significant attention in recent years. With the help of satellites, scientists and researchers can collect and store high resolution image data that can be further processed, segmented, and classified. However, these research results have not yet been synthesized to provide coherent guidance on the effect of variant land-cover segmentation processes. In this paper, we present a novel model that augments segmentation using smaller networks to segment individual classes. …

Stock Markets Performance During A Pandemic: How Contagious Is Covid-19?, Yara Abushahba

Theses and Dissertations

Background and Motivation: The coronavirus (“COVID-19”) pandemic, the subsequent policies and lockdowns have unarguably led to an unprecedented fluid circumstance worldwide. The panic and fluctuations in the stock markets were unparalleled. It is inarguable that real-time availability of news and social media platforms like Twitter played a vital role in driving the investors’ sentiment during such global shock.

Purpose:The purpose of this thesis is to study how the investor sentiment in relation to COVID-19 pandemic influenced stock markets globally and how stock markets globally are integrated and contagious. We analyze COVID-19 sentiment through the Twitter posts and investigate its …

Rm-Net: Rasterizing Markov Signals To Images For Deep Learning, Kajal Gupta

Theses

Statistical machine learning approaches are quite famous for processing Markov signal data. They can model unobserved states and learn certain characteristics particular to a signal with good accuracy. However, with the advent of Deep learning the novice ways of solving a problem has shifted towards this more sophisticated algorithm, which is much better, powerful and more accurate. Specifically, Convolutional Neural Nets (CNN) have shown many promising results on images and videos. Here we illustrate how CNN can be applied to a 1D numeric signal using signal rasterization technique. We start by rasterizing a 1D numeric Markov signal into an image …

Clickbait Detection In Youtube Videos, Ruchira Gothankar

Master's Projects

YouTube videos often include captivating descriptions and intriguing thumbnails designed to increase the number of views, and thereby increase the revenue for the person who posted the video. This creates an incentive for people to post clickbait videos, in which the content might deviate significantly from the title, description, or thumbnail. In effect, users are tricked into clicking on clickbait videos. In this research, we consider the challenging problem of detecting clickbait YouTube videos. We experiment with logistic regression, random forests, and multilayer perceptrons, based on a variety of textual features. We obtain a maximum accuracy in excess of 94%.

Can Parallel Gravitational Search Algorithm Effectively Choose Parameters For Photovoltaic Cell Current Voltage Characteristics?, Alan Kirkpatrick

Honors Projects

This study asks the question “Can parallel Gravitational Search Algorithm (GSA) effectively choose parameters for photovoltaic cell current voltage characteristics?” These parameters will be plugged into the Single Diode Model to create the IV curve. It will also investigate Particle Swarm Optimization (PSO) and a population based random search (PBRS) to see if GSA performs the search better and or more quickly than alternative algorithms

Malware Classification With Bert, Joel Lawrence Alvares

Master's Projects

Malware Classification is used to distinguish unique types of malware from each other.

This project aims to carry out malware classification using word embeddings which are used in Natural Language Processing (NLP) to identify and evaluate the relationship between words of a sentence. Word embeddings generated by BERT and Word2Vec for malware samples to carry out multi-class classification. BERT is a transformer based pre- trained natural language processing (NLP) model which can be used for a wide range of tasks such as question answering, paraphrase generation and next sentence prediction. However, the attention mechanism of a pre-trained BERT model can …

Keystroke Dynamics Based On Machine Learning, Han-Chih Chang

Master's Projects

The development of active and passive biometric authentication and identification technology plays an increasingly important role in cybersecurity. Biometrics that utilize features derived from keystroke dynamics have been studied in this context. Keystroke dynamics can be used to analyze the way that a user types by monitoring various keyboard inputs. Previous work has considered the feasibility of user authentication and classification based on keystroke features. In this research, we analyze a wide variety of machine learning and deep learning models based on keystroke-derived features, we optimize the resulting models, and we compare our results to those obtained in related research. …

Malware Analysis With Auxiliary-Classifier Gan, Rakesh Nagaraju

Master's Projects

A generative adversarial network (GAN) is a powerful machine learning concept where both a generative and discriminative model are trained simultaneously. A recent trend in malware research consists of treating executables as images and employing image-based analysis techniques. In this research, we generate fake malware images using GANs, and we also consider the effectiveness of GANs for malware classification. Specifically, we consider auxiliary classifier GAN (AC-GAN), which enables us to work with multiclass data. We find that AC-GAN generates malware images that cannot be reliably distinguished from real malware images. In addition, we find that the detection capabilities of AC-GAN …

Presentation Attack Detection In Facial Biometric Authentication, Hardik Kumar

Master's Projects

Biometric systems are referred to those structures that enable recognizing an individual, or specifically a characteristic, using biometric data and mathematical algorithms. These are known to be widely employed in various organizations and companies, mostly as authentication systems. Biometric authentic systems are usually much more secure than a classic one, however they also have some loopholes. Presentation attacks indicate those attacks which spoof the biometric systems or sensors. The presentation attacks covered in this project are: photo attacks and deepfake attacks. In the case of photo attacks, it is observed that interactive action check like Eye Blinking proves efficient in …

Machine Learning To Detect Malware Evolution, Lolitha Sresta Tupadha

Master's Projects

Malware evolves over time and anti-virus must adapt to such evolution. Hence, it is critical to detect those points in time where malware has evolved so that appro-priate countermeasures can be undertaken. In this research, we perform a variety of experiments to determine when malware evolution is likely to have occurred. All of the evolution detection techniques that we consider are based on machine learning and can be fully automated—in particular, no reverse engineering or other labor-intensive manual analysis is required. Specifically, we consider analysis based on hidden Markov models and various word embedding techniques, among other machine learning based …

Computer-Aided Diagnosis Of Low Grade Endometrial Stromal Sarcoma (Lgess), Xinxin Yang

Master's Projects

Low grade endometrial stromal sarcoma (LGESS) is rare form of cancer, account- ing for about 0.2% of all uterine cancer cases. Approximately 75% of LGESS patients are initially misdiagnosed with leiomyoma, which is a type of benign tumor that is also known as fibroids. In this research, uterine tissue biopsy images of potential LGESS patients are preprocessed using segmentation and staining normalization algorithms. A wide variety of classic machine learning and leading deep learning models are then applied to classify tissue images as either benign or cancerous. For classic techniques, the highest classification accuracy we attain is 85%, while our …

Hidden Markov Model-Based Clustering For Malware Classification, Shamli Singh

Master's Projects

Automated techniques to classify malware samples into their respective families are critical in cybersecurity. Previously research applied ��-means clustering to scores generated by hidden Markov models (HMM) as a means of dealing with the malware classification problem. In this research, we follow a somewhat similar approach, but instead of using HMMs to generate scores, we directly cluster the HMMs themselves. We obtain good results on a challenging malware dataset.

Classifying Illegal Advertisements On The Darknet Using Nlp, Karan Shashin Shah

Master's Projects

The Darknet has become a place to conduct various illegal activities like child labor, contract murder, drug selling while staying anonymous. Traditionally, international and government agencies try to control these activities, but most of those actions are manual and time-consuming. Recently, various researchers developed Machine Learning (ML) approaches trying to aid in the process of detecting illegal activities. The above problem can benefit by using different Natural Language Processing (NLP) techniques. More specifically, researchers have used various classical topic modeling techniques like bag of words, N-grams, Term Frequency, Term Frequency Inverse Document Frequency (TF-IDF) to represent features and train machine …

Fake Malware Classification With Cnn Via Image Conversion: A Game Theory Approach, Yash Sahasrabuddhe

Master's Projects

Improvements in malware detection techniques have grown significantly over the past decade. These improvements have resulted in better security for systems from various forms of malware attacks. However, it is also the reason for continuous evolution of malware which makes it harder for current security mechanisms to detect them. Hence, there is a need to understand different malwares and study classification techniques using the ever-evolving field of machine learning. The goal of this research project is to identify similarities between malware families and to improve on classification of malwares within different malware families by implementing Convolutional Neural Networks (CNNs) on …

Keystroke Dynamics For User Authentication With Fixed And Free Text, Jianwei Li

Master's Projects

YouTube videos often include captivating descriptions and intriguing thumbnails designed to increase the number of views, and thereby increase the revenue for the person who posted the video. This creates an incentive for people to post clickbait videos, in which the content might deviate significantly from the title, description, or thumbnail. In effect, users are tricked into clicking on clickbait videos. In this research, we consider the challenging problem of detecting clickbait YouTube videos. We experiment with multiple state of the art machine learning techniques and a variety of textual features.

Image-Based Real Estate Appraisal Using Cnns And Ensemble Learning, Prathamesh Dnyanesh Kumkar

Master's Projects

Real Estate Appraisal is performed to evaluate properties during a range of activities like buying, selling, mortgaging, or insuring. Traditionally, this process is done by real estate brokers who consider factors like the location of a house, its area, the number of bedrooms and bathrooms, along with other amenities to assess the property. This approach is quite subjective since different brokers may arrive at a different quote for the same property depending on their analysis. The development in machine learning algorithms has given rise to several Automated Valuation Models (AVMs) to estimate real estate prices. Real estate websites use such …

Translating Natural Language Queries To Sparql, Shreya Satish Bhajikhaye

Master's Projects

The Semantic Web is an extensive knowledge base that contains facts in the form of RDF
triples. These facts are not easily accessible to the average user because to use them requires
an understanding of ontologies and a query language like SPARQL. Question answering systems
form a layer of abstraction on linked data to overcome these issues. These systems allow the
user to input a question in a natural language and receive the equivalent SPARQL query. The
user can then execute the query on the database to fetch the desired results. The standard
techniques involved in translating natural language questions …

Detecting And Predicting Visual Affordance Of Objects In A Given Environment, Bhumika Kaur Matharu

Master's Projects

The rapid growth of the development of autonomous robots is transforming the manufacturing and healthcare industry in many ways, but they still face many challenges. One of the challenges experienced by autonomous robots is their inability to manipulate an unknown object without human supervision. One way through which autonomous robots can manipulate an unknown object is affordance learning [1]. Affordance describes the action a user can perform on the object in given surroundings. This report describes our proposed model to detect and predict the affordance of an object from videos by leveraging the spatial-temporal feature extraction through ConvLSTM and Fully …

Machine Learning Using Serverless Computing, Vidish Naik

Master's Projects

Machine learning has been trending in the domain of computer science for quite some time. Newer and newer models and techniques are being developed every day. The adoption of cloud computing has only expedited the process of training machine learning. With its variety of services, cloud computing provides many options for training machine learning models. Leveraging these services is up to the user. Serverless computing is an important service offered by cloud service providers. It is useful for short tasks that are event-driven or periodic. Machine learning training can be divided into short tasks or batches to take advantage of …

A Hybrid Gaze Pointer With Voice Control, Indhuja Ravi

Master's Projects

Accessibility in technology has been a challenge since the beginning of the 1800s. Starting with building typewriters for the blind by Pellegrino Turri to the on-screen keyboard built by Microsoft, there have been several advancements towards assistive technologies. The basic tools necessary for anyone to operate a computer are to be able to navigate the device, input information, and perceive the output. All these three categories have been undergoing tremendous advancements over the years. Especially, with the internet boom, it has now become a necessity to point onto a computer screen. This has somewhat attracted research into this particular area. …

Automating Text Encapsulation Using Deep Learning, Anket Sah

Master's Projects

Data is an important aspect in any form be it communication, reviews, news articles, social media data, machine or real-time data. With the emergence of Covid-19, a pandemic seen like no other in recent times, information is being poured in from all directions on the internet. At times it is overwhelming to determine which data to read and follow. Another crucial aspect is separating factual data from distorted data that is being circulated widely. The title or short description of this data can play a key role. Many times, these descriptions can deceive a user with unwanted information. The user …