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Artificial Intelligence and Robotics

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Articles 1 - 18 of 18

Full-Text Articles in Other Computer Engineering

Self-Driving Cars: Evaluation Of Deep Learning Techniques For Object Detection In Different Driving Conditions, Ramesh Simhambhatla, Kevin Okiah, Shravan Kuchkula, Robert Slater May 2019

Self-Driving Cars: Evaluation Of Deep Learning Techniques For Object Detection In Different Driving Conditions, Ramesh Simhambhatla, Kevin Okiah, Shravan Kuchkula, Robert Slater

SMU Data Science Review

Deep Learning has revolutionized Computer Vision, and it is the core technology behind capabilities of a self-driving car. Convolutional Neural Networks (CNNs) are at the heart of this deep learning revolution for improving the task of object detection. A number of successful object detection systems have been proposed in recent years that are based on CNNs. In this paper, an empirical evaluation of three recent meta-architectures: SSD (Single Shot multi-box Detector), R-CNN (Region-based CNN) and R-FCN (Region-based Fully Convolutional Networks) was conducted to measure how fast and accurate they are in identifying objects on the road, such as vehicles, pedestrians ...


Comparative Study Of Sentiment Analysis With Product Reviews Using Machine Learning And Lexicon-Based Approaches, Heidi Nguyen, Aravind Veluchamy, Mamadou Diop, Rashed Iqbal Jan 2019

Comparative Study Of Sentiment Analysis With Product Reviews Using Machine Learning And Lexicon-Based Approaches, Heidi Nguyen, Aravind Veluchamy, Mamadou Diop, Rashed Iqbal

SMU Data Science Review

In this paper, we present a comparative study of text sentiment classification models using term frequency inverse document frequency vectorization in both supervised machine learning and lexicon-based techniques. There have been multiple promising machine learning and lexicon-based techniques, but the relative goodness of each approach on specific types of problems is not well understood. In order to offer researchers comprehensive insights, we compare a total of six algorithms to each other. The three machine learning algorithms are: Logistic Regression (LR), Support Vector Machine (SVM), and Gradient Boosting. The three lexicon-based algorithms are: Valence Aware Dictionary and Sentiment Reasoner (VADER), Pattern ...


Automatic Identification Of Animals In The Wild: A Comparative Study Between C-Capsule Networks And Deep Convolutional Neural Networks., Joel Kamdem Teto, Ying Xie Nov 2018

Automatic Identification Of Animals In The Wild: A Comparative Study Between C-Capsule Networks And Deep Convolutional Neural Networks., Joel Kamdem Teto, Ying Xie

Master of Science in Computer Science Theses

The evolution of machine learning and computer vision in technology has driven a lot of

improvements and innovation into several domains. We see it being applied for credit decisions, insurance quotes, malware detection, fraud detection, email composition, and any other area having enough information to allow the machine to learn patterns. Over the years the number of sensors, cameras, and cognitive pieces of equipment placed in the wilderness has been growing exponentially. However, the resources (human) to leverage these data into something meaningful are not improving at the same rate. For instance, a team of scientist volunteers took 8.4 ...


Investigating Dataset Distinctiveness, Andrew Ulmer, Kent W. Gauen, Yung-Hsiang Lu, Zohar R. Kapach, Daniel P. Merrick Aug 2018

Investigating Dataset Distinctiveness, Andrew Ulmer, Kent W. Gauen, Yung-Hsiang Lu, Zohar R. Kapach, Daniel P. Merrick

The Summer Undergraduate Research Fellowship (SURF) Symposium

Just as a human might struggle to interpret another human’s handwriting, a computer vision program might fail when asked to perform one task in two different domains. To be more specific, visualize a self-driving car as a human driver who had only ever driven on clear, sunny days, during daylight hours. This driver – the self-driving car – would inevitably face a significant challenge when asked to drive when it is violently raining or foggy during the night, putting the safety of its passengers in danger. An extensive understanding of the data we use to teach computer vision models – such as ...


Comparative Study Of Deep Learning Models For Network Intrusion Detection, Brian Lee, Sandhya Amaresh, Clifford Green, Daniel Engels Apr 2018

Comparative Study Of Deep Learning Models For Network Intrusion Detection, Brian Lee, Sandhya Amaresh, Clifford Green, Daniel Engels

SMU Data Science Review

In this paper, we present a comparative evaluation of deep learning approaches to network intrusion detection. A Network Intrusion Detection System (NIDS) is a critical component of every Internet connected system due to likely attacks from both external and internal sources. A NIDS is used to detect network born attacks such as Denial of Service (DoS) attacks, malware replication, and intruders that are operating within the system. Multiple deep learning approaches have been proposed for intrusion detection systems. We evaluate three models, a vanilla deep neural net (DNN), self-taught learning (STL) approach, and Recurrent Neural Network (RNN) based Long Short ...


Dynamic Adversarial Mining - Effectively Applying Machine Learning In Adversarial Non-Stationary Environments., Tegjyot Singh Sethi Aug 2017

Dynamic Adversarial Mining - Effectively Applying Machine Learning In Adversarial Non-Stationary Environments., Tegjyot Singh Sethi

Electronic Theses and Dissertations

While understanding of machine learning and data mining is still in its budding stages, the engineering applications of the same has found immense acceptance and success. Cybersecurity applications such as intrusion detection systems, spam filtering, and CAPTCHA authentication, have all begun adopting machine learning as a viable technique to deal with large scale adversarial activity. However, the naive usage of machine learning in an adversarial setting is prone to reverse engineering and evasion attacks, as most of these techniques were designed primarily for a static setting. The security domain is a dynamic landscape, with an ongoing never ending arms race ...


Explorations Into Machine Learning Techniques For Precipitation Nowcasting, Aditya Nagarajan Jan 2017

Explorations Into Machine Learning Techniques For Precipitation Nowcasting, Aditya Nagarajan

Masters Theses

Recent advances in cloud-based big-data technologies now makes data driven solutions feasible for increasing numbers of scientific computing applications. One such data driven solution approach is machine learning where patterns in large data sets are brought to the surface by finding complex mathematical relationships within the data. Nowcasting or short-term prediction of rainfall in a given region is an important problem in meteorology. In this thesis we explore the nowcasting problem through a data driven approach by formulating it as a machine learning problem.

State-of-the-art nowcasting systems today are based on numerical models which describe the physical processes leading to ...


Front Matter: Proceedings Of The Maics 2016 Conference, University Of Dayton Apr 2016

Front Matter: Proceedings Of The Maics 2016 Conference, University Of Dayton

Content presented at the MAICS conference

Front matter contains:

  • A list of program chairs and committee members
  • Foreword to the proceedings by James P. Buckley, conference chair; Saverio Perugini, general chair

Editors: Phu H. Phung, University of Dayton; Ju Shen, University of Dayton; Michael Glass, Valparaiso University


Representation And Analysis Of Multi-Modal, Nonuniform Time Series Data: An Application To Survival Prognosis Of Oncology Patients In An Outpatient Setting, Jennifer Winikus Jan 2016

Representation And Analysis Of Multi-Modal, Nonuniform Time Series Data: An Application To Survival Prognosis Of Oncology Patients In An Outpatient Setting, Jennifer Winikus

Dissertations, Master's Theses and Master's Reports

The representation of nonuniform, multi-modal, time-limited time series data is complex and explored through the use of discrete representation, dimensionality reduction with segmentation based techniques, and with behavioral representation approaches. These explorations are done with a focus on an outpatient oncology setting with the classification and regression analysis being used for length of survival prognosis. Each decision of representation and analysis is not independent, with implications of each decision in method for how the data is represented and then which analysis technique is used. One unique aspect of the work is the use of outpatient clinical data for patients, which ...


Two Correspondence Problems Easier Than One, Aaron Michaux, Zygmunt Pizlo May 2015

Two Correspondence Problems Easier Than One, Aaron Michaux, Zygmunt Pizlo

MODVIS Workshop

Computer vision research rarely makes use of symmetry in stereo reconstruction despite its established importance in perceptual psychology. Such stereo reconstructions produce visually satisfying figures with precisely located points and lines, even when input images have low or moderate resolution. However, because few invariants exist, there are no known general approaches to solving symmetry correspondence on real images. The problem is significantly easier when combined with the binocular correspondence problem, because each correspondence problem provides strong non-overlapping constraints on the solution space. We demonstrate a system that leverages these constraints to produce accurate stereo models from pairs of binocular images ...


An Empirical Study Of Semantic Similarity In Wordnet And Word2vec, Abram Handler Dec 2014

An Empirical Study Of Semantic Similarity In Wordnet And Word2vec, Abram Handler

University of New Orleans Theses and Dissertations

This thesis performs an empirical analysis of Word2Vec by comparing its output to WordNet, a well-known, human-curated lexical database. It finds that Word2Vec tends to uncover more of certain types of semantic relations than others -- with Word2Vec returning more hypernyms, synonomyns and hyponyms than hyponyms or holonyms. It also shows the probability that neighbors separated by a given cosine distance in Word2Vec are semantically related in WordNet. This result both adds to our understanding of the still-unknown Word2Vec and helps to benchmark new semantic tools built from word vectors.


Openorbiter Operating Software, Dayln Limesand, Christoffer Korvald, Jeremy Straub, Ronald Marsh Mar 2014

Openorbiter Operating Software, Dayln Limesand, Christoffer Korvald, Jeremy Straub, Ronald Marsh

Jeremy Straub

The operating software team of the OpenOrbiter project has been tasked with developing software for general spacecraft maintenance, performing mission tasks and the monitoring of system critical aspects of the spacecraft. To do so, the team is developing an autonomous system that will be able to continuously check sensors for data, and schedule tasks that pertain to the current mission and general maintenance of the onboard systems. Development in support of these objectives is ongoing with work focusing on the completion of the development of a stable system. This poster will present an overview of current work on the project ...


Online Multi-Stage Deep Architectures For Feature Extraction And Object Recognition, Derek Christopher Rose Aug 2013

Online Multi-Stage Deep Architectures For Feature Extraction And Object Recognition, Derek Christopher Rose

Doctoral Dissertations

Multi-stage visual architectures have recently found success in achieving high classification accuracies over image datasets with large variations in pose, lighting, and scale. Inspired by techniques currently at the forefront of deep learning, such architectures are typically composed of one or more layers of preprocessing, feature encoding, and pooling to extract features from raw images. Training these components traditionally relies on large sets of patches that are extracted from a potentially large image dataset. In this context, high-dimensional feature space representations are often helpful for obtaining the best classification performances and providing a higher degree of invariance to object transformations ...


A Human Proximity Operations System Test Case Validation Approach, Justin Huber, Jeremy Straub Mar 2013

A Human Proximity Operations System Test Case Validation Approach, Justin Huber, Jeremy Straub

Jeremy Straub

A Human Proximity Operations System (HPOS) poses numerous risks in a real world environment. These risks range from mundane tasks such as avoiding walls and fixed obstacles to the critical need to keep people and processes safe in the context of the HPOS’s situation-specific decision making. Validating the performance of an HPOS, which must operate in a real-world environment, is an ill posed problem due to the complexity that is introduced by erratic (non-computer) actors. In order to prove the HPOS’s usefulness, test cases must be generated to simulate possible actions of these actors, so the HPOS can ...


Exposing Multiple User-Specific Data Denominated Products From A Single Small Satellite Data Stream, Atif F. Mohammad,, Emanuel Grant, Jeremy Straub, Ronald Marsh, Scott Kerlin Mar 2013

Exposing Multiple User-Specific Data Denominated Products From A Single Small Satellite Data Stream, Atif F. Mohammad,, Emanuel Grant, Jeremy Straub, Ronald Marsh, Scott Kerlin

Jeremy Straub

This paper presents a research work on small satellite data stream and related distribution to associated stakeholders, which is a field that needs to get explored in more detail. The algorithm that is presented to extract USDDP (User-Specific Data Denominated Products) is a self managing body, which will be within as Open Space Box environment or OSBE as a novel idea. It contains an individual stream transmitted by the small satellite, which later is to be converted into USDDP. The context defined here deals with area in detail. Contexts are vitally important because they control, influence and affect everything within ...


Open Space Box Model: Service Oriented Architecture Framework For Small Spacecraft Collaboration And Control, Atif F. Mohammad, Jeremy Straub Feb 2013

Open Space Box Model: Service Oriented Architecture Framework For Small Spacecraft Collaboration And Control, Atif F. Mohammad, Jeremy Straub

Jeremy Straub

A Cubesat is a small satellite with very less competence to compute, it requires software engineering techniques, which can enhance the computational power for this small box. A model-driven approach of software engineering, which is called OSBM or Open Space Box Modeling technique, is an excellent solution to this re-source maximization challenge. OSBM facilitates apparition of the key solution pro-cesses computation and satellite related data elements using Service Oriented Ar-chitecture 3.0 (SOA 3.0) as base to work on to design services. The key challenges that can be handled by utilizing OSBM include concurrent operation and tasking of few ...


Operating Software, Donovan Torgerson, Miyuru Arangala, Michael Hlas, David Bullock, Dayln Limesand, Cameron Kerbaugh, Daniel Schuler, Mitchell Fossen, Edwin Carlson, Atif Mohammad, Josh Berk, Jeremy Straub Jan 2012

Operating Software, Donovan Torgerson, Miyuru Arangala, Michael Hlas, David Bullock, Dayln Limesand, Cameron Kerbaugh, Daniel Schuler, Mitchell Fossen, Edwin Carlson, Atif Mohammad, Josh Berk, Jeremy Straub

Jeremy Straub

No abstract provided.


Cubesat Software Architecture, Christoffer Korvald, Atif Mohammad, Jeremy Straub, Josh Berk Jan 2012

Cubesat Software Architecture, Christoffer Korvald, Atif Mohammad, Jeremy Straub, Josh Berk

Jeremy Straub

No abstract provided.