Matching Items (81)

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An Introduction to Machine Vision in Multirotors

Description

In the last decade, a large variety of algorithms have been developed for use in object tracking, environment mapping, and object classification. It is often difficult for beginners to fully predict the constraints that multirotors place on machine vision

In the last decade, a large variety of algorithms have been developed for use in object tracking, environment mapping, and object classification. It is often difficult for beginners to fully predict the constraints that multirotors place on machine vision algorithms. The purpose of this paper is to explain some of the types of algorithms that can be applied to these aerial systems, why the constraints for these algorithms exist, and what could be done to mitigate them. This paper provides a summary of the processes involved in a popular filter-based tracking algorithm called MOSSE (Minimum Output Sum of Squared Error) and a particular implementation of SLAM (Simultaneous Localization and Mapping) called LSD SLAM.

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2020-05

Detecting Propaganda Bots on Twitter Using Machine Learning

Description

Propaganda bots are malicious bots on Twitter that spread divisive opinions and support political accounts. This project is based on detecting propaganda bots on Twitter using machine learning. Once I began to observe patterns within propaganda followers on

Propaganda bots are malicious bots on Twitter that spread divisive opinions and support political accounts. This project is based on detecting propaganda bots on Twitter using machine learning. Once I began to observe patterns within propaganda followers on Twitter, I determined that I could train algorithms to detect these bots. The paper focuses on my development and process of training classifiers and using them to create a user-facing server that performs prediction functions automatically. The learning goals of this project were detailed, the focus of which was to learn some form of machine learning architecture. I needed to learn some aspect of large data handling, as well as being able to maintain these datasets for training use. I also needed to develop a server that would execute these functionalities on command. I wanted to be able to design a full-stack system that allowed me to create every aspect of a user-facing server that can execute predictions using the classifiers that I design.
Throughout this project, I decided on a number of learning goals to consider it a success. I needed to learn how to use the supporting libraries that would help me to design this system. I also learned how to use the Twitter API, as well as create the infrastructure behind it that would allow me to collect large amounts of data for machine learning. I needed to become familiar with common machine learning libraries in Python in order to create the necessary algorithms and pipelines to make predictions based on Twitter data.
This paper details the steps and decisions needed to determine how to collect this data and apply it to machine learning algorithms. I determined how to create labelled data using pre-existing Botometer ratings, and the levels of confidence I needed to label data for training. I use the scikit-learn library to create these algorithms to best detect these bots. I used a number of pre-processing routines to refine the classifiers’ precision, including natural language processing and data analysis techniques. I eventually move to remotely-hosted versions of the system on Amazon web instances to collect larger amounts of data and train more advanced classifiers. This leads to the details of my final implementation of a user-facing server, hosted on AWS and interfacing over Gmail’s IMAP server.
The current and future development of this system is laid out. This includes more advanced classifiers, better data analysis, conversions to third party Twitter data collection systems, and user features. I detail what it is I have learned from this exercise, and what it is I hope to continue working on.

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Date Created
2019-05

Deep Periodic Networks

Description

In the field of machine learning, reinforcement learning stands out for its ability to explore approaches to complex, high dimensional problems that outperform even expert humans. For robotic locomotion tasks reinforcement learning provides an approach to solving them without the

In the field of machine learning, reinforcement learning stands out for its ability to explore approaches to complex, high dimensional problems that outperform even expert humans. For robotic locomotion tasks reinforcement learning provides an approach to solving them without the need for unique controllers. In this thesis, two reinforcement learning algorithms, Deep Deterministic Policy Gradient and Group Factor Policy Search are compared based upon their performance in the bipedal walking environment provided by OpenAI gym. These algorithms are evaluated on their performance in the environment and their sample efficiency.

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Date Created
2018-12

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Comparative Analysis in Acquisition of Coding Skills

Description

Students learn in various ways \u2014 visualization, auditory, memorizing, or making analogies. Traditional lecturing in engineering courses and the learning styles of engineering students are inharmonious causing students to be at a disadvantage based on their learning style (Felder &

Students learn in various ways \u2014 visualization, auditory, memorizing, or making analogies. Traditional lecturing in engineering courses and the learning styles of engineering students are inharmonious causing students to be at a disadvantage based on their learning style (Felder & Silverman, 1988). My study analyzes the traditional approach to learning coding skills which is unnatural to engineering students with no previous exposure and examining if visual learning enhances introductory computer science education. Visual and text-based learning are evaluated to determine how students learn introductory coding skills and associated problem solving skills. My study was conducted to observe how the two types of learning aid the students in learning how to problem solve as well as how much knowledge can be obtained in a short period of time. The application used for visual learning was Scratch and Repl.it was used for text-based learning. Two exams were made to measure the progress made by each student. The topics covered by the exam were initialization, variable reassignment, output, if statements, if else statements, nested if statements, logical operators, arrays/lists, while loop, type casting, functions, object orientation, and sorting. Analysis of the data collected in the study allow us to observe whether the traditional method of teaching programming or block-based programming is more beneficial and in what topics of introductory computer science concepts.

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2018-05

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Ambient Intelligence: Synthesizing All Aspects of Life By Making Our Environment ""Smarter""

Description

All of the modern technology tools that are being used today, have a purpose to support a variety of human tasks. Ambient Intelligence is the next step to transform modern technology. Ambient Intelligence is an electronic environment that is sensitive

All of the modern technology tools that are being used today, have a purpose to support a variety of human tasks. Ambient Intelligence is the next step to transform modern technology. Ambient Intelligence is an electronic environment that is sensitive and responsive to human interaction/activity. We understand that Ambient Intelligence(AmI) concentrates on connectivity within a person's environment and the purpose of having a new connection is to make life simpler. Today, technology is in the transition of a new lifestyle where technology is discretely living with us. Ambient Intelligence is still in progress, but we can analyze the technology we have today, ties a relationship with Ambient Intelligence. In order to examine this concern, I investigated how much awareness/knowledge users that range from Generation X to Xennials, that had experience from replacing habitual items and technologies they use on a daily basis. A few questions I mainly wanted answered: - What kind of technologies, software, or tech services replace items you use daily? - What kind of benefits did the technology give you, did it change the way you think/act on any kind of activities? - What kind of expectations/concerns do you have for future technologies? To accomplish this, I gathered information from interviewing multiples groups: millennials and other older generations (33+ years old). I retrieved data from students at Arizona State University, Intel Corporation, and a local clinic. From this study, I've discovered from both groups, that both sides agree that modern technology is rapidly growing to a point that computers think as humans. Through multiple interviews and research, I have found that the technology today makes an impact through all aspects of our lives and through artificial intelligence. Furthermore, I will discuss and predict what will society will encounter later on as the new technology discretely arises.

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2018-05

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A Framework for Measuring Human Uncertainty of Autonomous Vehicles with Specific Attention to the Inclusion of Empathy: Can Human Eyes Reveal Surprise?

Description

Currently, autonomous vehicles are being evaluated by how well they interact with humans without evaluating how well humans interact with them. Since people are not going to unanimously switch over to using autonomous vehicles, attention must be given to how

Currently, autonomous vehicles are being evaluated by how well they interact with humans without evaluating how well humans interact with them. Since people are not going to unanimously switch over to using autonomous vehicles, attention must be given to how well these new vehicles signal intent to human drivers from the driver’s point of view. Ineffective communication will lead to unnecessary discomfort among drivers caused by an underlying uncertainty about what an autonomous vehicle is or isn’t about to do. Recent studies suggest that humans tend to fixate on areas of higher uncertainty so scenarios that have a higher number of vehicle fixations can be reasoned to be more uncertain. We provide a framework for measuring human uncertainty and use the framework to measure the effect of empathetic vs non-empathetic agents. We used a simulated driving environment to create recorded scenarios and manipulate the autonomous vehicle to include either an empathetic or non-empathetic agent. The driving interaction is composed of two vehicles approaching an uncontrolled intersection. These scenarios were played to twelve participants while their gaze was recorded to track what the participants were fixating on. The overall intent was to provide an analytical framework as a tool for evaluating autonomous driving features; and in this case, we choose to evaluate how effective it was for vehicles to have empathetic behaviors included in the autonomous vehicle decision making. A t-test analysis of the gaze indicated that empathy did not in fact reduce uncertainty although additional testing of this hypothesis will be needed due to the small sample size.

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2021-05

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Moving Target Defense: Defending against Adversarial Defense

Description

A defense-by-randomization framework is proposed as an effective defense mechanism against different types of adversarial attacks on neural networks. Experiments were conducted by selecting a combination of differently constructed image classification neural networks to observe which combinations applied to this

A defense-by-randomization framework is proposed as an effective defense mechanism against different types of adversarial attacks on neural networks. Experiments were conducted by selecting a combination of differently constructed image classification neural networks to observe which combinations applied to this framework were most effective in maximizing classification accuracy. Furthermore, the reasons why particular combinations were more effective than others is explored.

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Date Created
2019-05

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iLieDown - Improved Display Orientation For Handheld Devices Using Convolutional Neural Networks.pdf

Description

91% of smartphone and tablet users experience a problem with their device screen being oriented the wrong way during use [11]. In [11], the authors proposed iRotate, a previous solution which uses computer vision to solve the orientation problem. We

91% of smartphone and tablet users experience a problem with their device screen being oriented the wrong way during use [11]. In [11], the authors proposed iRotate, a previous solution which uses computer vision to solve the orientation problem. We propose iLieDown, an improved method of automatically rotating smartphones, tablets, and other device displays. This paper introduces a new algorithm to correctly orient the display relative to the user’s face using a convolutional neural network (CNN). The CNN model is trained to predict the rotation of faces in various environments through data augmentation, uses a confidence threshold, and analyzes multiple images to be accurate and robust. iLieDown is battery and CPU efficient, causes no noticeable lag to the user during use, and is 6x more accurate than iRotate.

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2019-12

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Computationally Efficient Object Detection Strategy from Water Surfaces with Specularity Removal

Description

Floating trash objects are very commonly seen on water bodies such as lakes, canals and rivers. With the increase of plastic goods and human activities near the water bodies, these trash objects can pile up and cause great harm to

Floating trash objects are very commonly seen on water bodies such as lakes, canals and rivers. With the increase of plastic goods and human activities near the water bodies, these trash objects can pile up and cause great harm to the surrounding environment. Using human workers to clear out these trash is a hazardous and time-consuming task. Employing autonomous robots for these tasks is a better approach since it is more efficient and faster than humans. However, for a robot to clean the trash objects, a good detection algorithm is required. Real-time object detection on water surfaces is a challenging issue due to nature of the environment and the volatility of the water surface. In addition to this, running an object detection algorithm on an on-board processor of a robot limits the amount of CPU consumption that the algorithm can utilize. In this thesis, a computationally low cost object detection approach for robust detection of trash objects that was run on an on-board processor of a multirotor is presented. To account for specular reflections on the water surface, we use a polarization filter and integrate a specularity removal algorithm on our approach as well. The challenges faced during testing and the means taken to eliminate those challenges are also discussed. The algorithm was compared with two other object detectors using 4 different metrics. The testing was carried out using videos of 5 different objects collected at different illumination conditions over a lake using a multirotor. The results indicate that our algorithm is much suitable to be employed in real-time since it had the highest processing speed of 21 FPS, the lowest CPU consumption of 37.5\% and considerably high precision and recall values in detecting the object.

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Date Created
2021

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Weakly-Supervised Visual-Retriever-Reader Pipeline for Knowledge-Based VQA Tasks

Description

Visual question answering (VQA) is a task that answers the questions by giving an image, and thus involves both language and vision methods to solve, which make the VQA tasks a frontier interdisciplinary field. In recent years, as the great

Visual question answering (VQA) is a task that answers the questions by giving an image, and thus involves both language and vision methods to solve, which make the VQA tasks a frontier interdisciplinary field. In recent years, as the great progress made in simple question tasks (e.g. object recognition), researchers start to shift their interests to the questions that require knowledge and reasoning. Knowledge-based VQA requires answering questions with external knowledge in addition to the content of images. One dataset that is mostly used in evaluating knowledge-based VQA is OK-VQA, but it lacks a gold standard knowledge corpus for retrieval. Existing work leverages different knowledge bases (e.g., ConceptNet and Wikipedia) to obtain external knowledge. Because of varying knowledge bases, it is hard to fairly compare models' performance. To address this issue, this paper collects a natural language knowledge base that can be used for any question answering (QA) system. Moreover, a Visual Retriever-Reader pipeline is proposed to approach knowledge-based VQA, where the visual retriever aims to retrieve relevant knowledge, and the visual reader seeks to predict answers based on given knowledge. The retriever is constructed with two versions: term based retriever which uses best matching 25 (BM25), and neural based retriever where the latest dense passage retriever (DPR) is introduced. To encode the visual information, the image and caption are encoded separately in the two kinds of neural based retriever: Image-DPR and Caption-DPR. There are also two styles of readers, classification reader and extraction reader. Both the retriever and reader are trained with weak supervision. The experimental results show that a good retriever can significantly improve the reader's performance on the OK-VQA challenge.

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2021