Artificial Intelligence and Robotics Commons^™

Visual Odometry Using Convolutional Neural Networks, Alec Graves, Steffen Lim, Thomas Fagan, Kevin Mcfall Phd.

The Kennesaw Journal of Undergraduate Research

Visual odometry is the process of tracking an agent's motion over time using a visual sensor. The visual odometry problem has only been recently solved using traditional, non-machine learning techniques. Despite the success of neural networks at many related problems such as object recognition, feature detection, and optical flow, visual odometry still has not been solved with a deep learning technique. This paper attempts to implement several Convolutional Neural Networks to solve the visual odometry problem and compare slight variations in data preprocessing. The work presented is a step toward reaching a legitimate neural network solution.

Automated Species Classification Methods For Passive Acoustic Monitoring Of Beaked Whales, John Lebien

University of New Orleans Theses and Dissertations

The Littoral Acoustic Demonstration Center has collected passive acoustic monitoring data in the northern Gulf of Mexico since 2001. Recordings were made in 2007 near the Deepwater Horizon oil spill that provide a baseline for an extensive study of regional marine mammal populations in response to the disaster. Animal density estimates can be derived from detections of echolocation signals in the acoustic data. Beaked whales are of particular interest as they remain one of the least understood groups of marine mammals, and relatively few abundance estimates exist. Efficient methods for classifying detected echolocation transients are essential for mining long-term passive …

Knowledge Driven Approaches And Machine Learning Improve The Identification Of Clinically Relevant Somatic Mutations In Cancer Genomics, Benjamin John Ainscough

Arts & Sciences Electronic Theses and Dissertations

For cancer genomics to fully expand its utility from research discovery to clinical adoption, somatic variant detection pipelines must be optimized and standardized to ensure identification of clinically relevant mutations and to reduce laborious and error-prone post-processing steps. To address the need for improved catalogues of clinically and biologically important somatic mutations, we developed DoCM, a Database of Curated Mutations in Cancer (http://docm.info), as described in Chapter 2. DoCM is an open source, openly licensed resource to enable the cancer research community to aggregate, store and track biologically and clinically important cancer variants. DoCM is currently comprised of 1,364 variants …

Ethics And Bias In Machine Learning: A Technical Study Of What Makes Us “Good”, Ashley Nicole Shadowen

Student Theses

The topic of machine ethics is growing in recognition and energy, but bias in machine learning algorithms outpaces it to date. Bias is a complicated term with good and bad connotations in the field of algorithmic prediction making. Especially in circumstances with legal and ethical consequences, we must study the results of these machines to ensure fairness. This paper attempts to address ethics at the algorithmic level of autonomous machines. There is no one solution to solving machine bias, it depends on the context of the given system and the most reasonable way to avoid biased decisions while maintaining the …

Developing Leading And Lagging Indicators To Enhance Equipment Reliability In A Lean System, Dhanush Agara Mallesh

Masters Theses

With increasing complexity in equipment, the failure rates are becoming a critical metric due to the unplanned maintenance in a production environment. Unplanned maintenance in manufacturing process is created issues with downtimes and decreasing the reliability of equipment. Failures in equipment have resulted in the loss of revenue to organizations encouraging maintenance practitioners to analyze ways to change unplanned to planned maintenance. Efficient failure prediction models are being developed to learn about the failures in advance. With this information, failures predicted can reduce the downtimes in the system and improve the throughput.

The goal of this thesis is to predict …

The Synthesis Of Memristive Neuromorphic Circuits, Austin Richard Wyer

Masters Theses

As Moores Law has come to a halt, it has become necessary to explore alternative forms of computation that are not limited in the same ways as traditional CMOS technologies and the Von Neumann architecture. Neuromorphic computing, computing inspired by the human brain with neurons and synapses, has been proposed as one of these alternatives. Memristors, non-volatile devices with adjustable resistances, have emerged as a candidate for implementing neuromorphic computing systems because of their low power and low area overhead. This work presents a C++ simulator for an implementation of a memristive neuromorphic circuit. The simulator is used within a …

Authorship Identification Of Translation Algorithms., Keishin Nishiyama

Electronic Theses and Dissertations

Authorship analysis is a process of identifying a true writer of a given document and has been studied for decades. However, only a handful of studies of authorship analysis of translators are available despite the fact that online translations are widely available and also popularly employed in automatic translations of posts in social networking services. The identification of translation algorithms has potential to contribute to the investigation of cybercrimes, involving translation of scam messages by algorithmic translations to reach speakers of foreign languages. This study tested bag of words (BOW) approach in authorship attribution and the existing approaches to translator …

Process Models Discovery And Traces Classification: A Fuzzy-Bpmn Mining Approach., Kingsley Okoye Dr, Usman Naeem Dr, Syed Islam Dr, Abdel-Rahman H. Tawil Dr, Elyes Lamine Dr

Journal of International Technology and Information Management

The discovery of useful or worthwhile process models must be performed with due regards to the transformation that needs to be achieved. The blend of the data representations (i.e data mining) and process modelling methods, often allied to the field of Process Mining (PM), has proven to be effective in the process analysis of the event logs readily available in many organisations information systems. Moreover, the Process Discovery has been lately seen as the most important and most visible intellectual challenge related to the process mining. The method involves automatic construction of process models from event logs about any domain …

An Unmanned Aerial System For Prescribed Fires, Evan M. Beachly

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Prescribed fires can lessen wildfire severity and control invasive species, but some terrains may be difficult, dangerous, or costly to burn with existing tools. This thesis presents the design of an unmanned aerial system that can ignite prescribed fires from the air, with less cost and risk than with aerial ignition from a manned aircraft. The prototype was evaluated in-lab and successfully used to ignite interior areas of two prescribed fires. Additionally, we introduce an approach that integrates a lightweight fire simulation to autonomously plan safe flight trajectories and suggest effective fire lines. Both components are unique in that they …

Efficient Gate System Operations For A Multipurpose Port Using Simulation Optimization, Ketki Kulkarni, Trong Khiem Tran, Hai Wang, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Port capacity is determined by three major infrastructural resources namely, berths, yards and gates. Theadvertised capacity is constrained by the least of the capacities of the three resources. While a lot ofattention has been paid to optimizing berth and yard capacities, not much attention has been given toanalyzing the gate capacity. The gates are a key node between the land-side and sea-side operations in anocean-to-cities value chain. The gate system under consideration, located at an important port in an Asiancity, is a multi-class parallel queuing system with non-homogeneous Poisson arrivals. It is hard to obtaina closed form analytic approach for …

Multilingual Sentiment Analysis : From Formal To Informal And Scarce Resource Languages, Siaw Ling Lo, Erik Cambria, Raymond Chiong, David Cornforth

Research Collection School Of Computing and Information Systems

The ability to analyse online user-generated content related to sentiments (e.g., thoughts and opinions) on products or policies has become a de-facto skillset for many companies and organisations. Besides the challenge of understanding formal textual content, it is also necessary to take into consideration the informal and mixed linguistic nature of online social media languages, which are often coupled with localised slang as a way to express ‘true’ feelings. Due to the multilingual nature of social media data, analysis based on a single official language may carry the risk of not capturing the overall sentiment of online content. While efforts …

Pose Guided Person Image Generation, Liqian Ma, Xu Jia, Qianru Sun, Bernt Schiele, Tinne Tuytelaars, Luc Van Gool

Research Collection School Of Computing and Information Systems

This paper proposes the novel Pose Guided Person Generation Network (PG$^2$) that allows to synthesize person images in arbitrary poses, based on an image of that person and a novel pose. Our generation framework PG^2 utilizes the pose information explicitly and consists of two key stages: pose integration and image refinement. In the first stage the condition image and the target pose are fed into a U-Net-like network to generate an initial but coarse image of the person with the target pose. The second stage then refines the initial and blurry result by training a U-Net-like generator in an adversarial …

Policy Gradient With Value Function Approximation For Collective Multiagent Planning, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Decentralized (PO)MDPs provide an expressive framework for sequential decision making in a multiagent system. Given their computational complexity, recent research has focused on tractable yet practical subclasses of Dec-POMDPs. We address such a subclass called CDec-POMDP where the collective behavior of a population of agents affects the joint-reward and environment dynamics. Our main contribution is an actor-critic (AC) reinforcement learning method for optimizing CDec-POMDP policies. Vanilla AC has slow convergence for larger problems. To address this, we show how a particular decomposition of the approximate action-value function over agents leads to effective updates, and also derive a new way to …

A Compact Representation Of Human Actions By Sliding Coordinate Coding, Runwei Ding, Qianru Sun, Mengyuan Liu, Hong Liu

Research Collection School Of Computing and Information Systems

Human action recognition remains challenging in realistic videos, where scale and viewpoint changes make the problem complicated. Many complex models have been developed to overcome these difficulties, while we explore using low-level features and typical classifiers to achieve the state-of-the-art performance. The baseline model of feature encoding for action recognition is bag-of-words model, which has shown high efficiency but ignores the arrangement of local features. Refined methods compensate for this problem by using a large number of co-occurrence descriptors or a concatenation of the local distributions in designed segments. In contrast, this article proposes to encode the relative position of …

Home Health Care Delivery Problem, Aldy Gunawan, Hoong Chuin Lau, Kun Lu

Research Collection School Of Computing and Information Systems

We address the Home Health Care Delivery Problem (HHCDP), which is concerned with staff scheduling in the home health care industry. The goal is to schedule health care providers to serve patients at their homes that maximizes the total collected preference scores from visited patients subject to several constraints, such as workload of the health care providers, time budget for each provider and so on. The complexity lies in the possibility of cancellation of patient bookings dynamically, and the generated schedule should attempt to patients’ preferred time windows. To cater to these requirements, we model the preference score as a …

A Selective-Discrete Particle Swarm Optimization Algorithm For Solving A Class Of Orienteering Problems, Aldy Gunawan, Vincent F. Yu, Perwira Redi, Parida Jewpanya, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

This study addresses a class of NP-hard problem called the Orienteering Problem (OP), which belongs to a well-known class of vehicle routing problems. In the OP, a set of nodes that associated with a location and a score is given. The time required to travel between each pair of nodes is known in advance. The total travel time is limited by a predetermined time budget. The objective is to select a subset of nodes to be visited that maximizes the total collected score within a path. The Team OP (TOP) is an extension of OP that incorporates multiple paths. Another …

Leveraging The Trade-Off Between Accuracy And Interpretability In A Hybrid Intelligent System, Di Wang, Chai Quek, Ah-Hwee Tan, Chunyan Miao, Geok See Ng, You Zhou

Research Collection School Of Computing and Information Systems

Neural Fuzzy Inference System (NFIS) is a widely adopted paradigm to develop a data-driven learning system. This hybrid system has been widely adopted due to its accurate reasoning procedure and comprehensible inference rules. Although most NFISs primarily focus on accuracy, we have observed an ever increasing demand on improving the interpretability of NFISs and other types of machine learning systems. In this paper, we illustrate how we leverage the trade-off between accuracy and interpretability in an NFIS called Genetic Algorithm and Rough Set Incorporated Neural Fuzzy Inference System (GARSINFIS). In a nutshell, GARSINFIS self-organizes its network structure with a small …

On Improvised Music, Computational Creativity And Human-Becoming, Arto Artinian, Adam James Wilson

Publications and Research

Music improvisation is an act of human-becoming: of self-expression—an articulation of histories and memories that have molded its participants—and of exploration—a search for unimagined structures that break with the stale norms of majoritarian culture. Given that the former objective may inhibit the latter, we propose an integration of human musical improvisers and deliberately flawed creative software agents that are designed to catalyze the development of human-ratified minoritarian musical structures.

Graph-Based Latent Embedding, Annotation And Representation Learning In Neural Networks For Semi-Supervised And Unsupervised Settings, Ismail Ozsel Kilinc

USF Tampa Graduate Theses and Dissertations

Machine learning has been immensely successful in supervised learning with outstanding examples in major industrial applications such as voice and image recognition. Following these developments, the most recent research has now begun to focus primarily on algorithms which can exploit very large sets of unlabeled examples to reduce the amount of manually labeled data required for existing models to perform well. In this dissertation, we propose graph-based latent embedding/annotation/representation learning techniques in neural networks tailored for semi-supervised and unsupervised learning problems. Specifically, we propose a novel regularization technique called Graph-based Activity Regularization (GAR) and a novel output layer modification called …

Nbpmf: Novel Network-Based Inference Methods For Peptide Mass Fingerprinting, Zhewei Liang

Electronic Thesis and Dissertation Repository

Proteins are large, complex molecules that perform a vast array of functions in every living cell. A proteome is a set of proteins produced in an organism, and proteomics is the large-scale study of proteomes. Several high-throughput technologies have been developed in proteomics, where the most commonly applied are mass spectrometry (MS) based approaches. MS is an analytical technique for determining the composition of a sample. Recently it has become a primary tool for protein identification, quantification, and post translational modification (PTM) characterization in proteomics research. There are usually two different ways to identify proteins: top-down and bottom-up. Top-down approaches …

Context-Based Human Activity Recognition Using Multimodal Wearable Sensors, Pratool Bharti

USF Tampa Graduate Theses and Dissertations

In the past decade, Human Activity Recognition (HAR) has been an important part of the regular day to day life of many people. Activity recognition has wide applications in the field of health care, remote monitoring of elders, sports, biometric authentication, e-commerce and more. Each HAR application needs a unique approach to provide solutions driven by the context of the problem. In this dissertation, we are primarily discussing two application of HAR in different contexts. First, we design a novel approach for in-home, fine-grained activity recognition using multimodal wearable sensors on multiple body positions, along with very small Bluetooth beacons …

Knowledge Representation And Reasoning With Deep Neural Networks, Arvind Ramanathan Neelakantan

Doctoral Dissertations

Knowledge representation and reasoning is one of the central challenges of artificial intelligence, and has important implications in many fields including natural language understanding and robotics. Representing knowledge with symbols, and reasoning via search and logic has been the dominant paradigm for many decades. In this work, we use deep neural networks to learn to both represent symbols and perform reasoning end-to-end from data. By learning powerful non-linear models, our approach generalizes to massive amounts of knowledge and works well with messy real-world data using minimal human effort. First, we show that recurrent neural networks with an attention mechanism achieve …

Deep-Learned Generative Representations Of 3d Shape Families, Haibin Huang

Doctoral Dissertations

Digital representations of 3D shapes are becoming increasingly useful in several emerging applications, such as 3D printing, virtual reality and augmented reality. However, traditional modeling softwares require users to have extensive modeling experience, artistic skills and training to handle their complex interfaces and perform the necessary low-level geometric manipulation commands. Thus, there is an emerging need for computer algorithms that help novice and casual users to quickly and easily generate 3D content. In this work, I will present deep learning algorithms that are capable of automatically inferring parametric representations of shape families, which can be used to generate new 3D …

Adaft: A Resource-Efficient Framework For Adaptive Fault-Tolerance In Cyber-Physical Systems, Ye Xu

Doctoral Dissertations

Cyber-physical systems frequently have to use massive redundancy to meet application requirements for high reliability. While such redundancy is required, it can be activated adaptively, based on the current state of the controlled plant. Most of the time the physical plant is in a state that allows for a lower level of fault-tolerance. Avoiding the continuous deployment of massive fault-tolerance will greatly reduce the workload of CPSs. In this dissertation, we demonstrate a software simulation framework (AdaFT) that can automatically generate the sub-spaces within which our adaptive fault-tolerance can be applied. We also show the theoretical benefits of AdaFT, and …

Controversy Analysis And Detection, Shiri Dori-Hacohen

Doctoral Dissertations

Seeking information on a controversial topic is often a complex task. Alerting users about controversial search results can encourage critical literacy, promote healthy civic discourse and counteract the "filter bubble" effect, and therefore would be a useful feature in a search engine or browser extension. Additionally, presenting information to the user about the different stances or sides of the debate can help her navigate the landscape of search results beyond a simple "list of 10 links". This thesis has made strides in the emerging niche of controversy detection and analysis. The body of work in this thesis revolves around two …

Graph Construction For Manifold Discovery, Cj Carey

Doctoral Dissertations

Manifold learning is a class of machine learning methods that exploits the observation that high-dimensional data tend to lie on a smooth lower-dimensional manifold. Manifold discovery is the essential first component of manifold learning methods, in which the manifold structure is inferred from available data. This task is typically posed as a graph construction problem: selecting a set of vertices and edges that most closely approximates the true underlying manifold. The quality of this learned graph is critical to the overall accuracy of the manifold learning method. Thus, it is essential to develop accurate, efficient, and reliable algorithms for constructing …

Deep Energy-Based Models For Structured Prediction, David Belanger

Doctoral Dissertations

We introduce structured prediction energy networks (SPENs), a flexible frame- work for structured prediction. A deep architecture is used to define an energy func- tion over candidate outputs and predictions are produced by gradient-based energy minimization. This deep energy captures dependencies between labels that would lead to intractable graphical models, and allows us to automatically discover discrim- inative features of the structured output. Furthermore, practitioners can explore a wide variety of energy function architectures without having to hand-design predic- tion and learning methods for each model. This is because all of our prediction and learning methods interact with the energy …

Intent Detection Through Text Mining And Analysis, Samantha Akulick, El Sayed Mahmoud

Publications and Scholarship

The article is about the work investigated using n-grams, parts-Of-Speech and Support Vector machines for detecting the customer intents in the user generated contents. The work demonstrated a system of categorization of customer intents that is concise and useful for business purposes. We examined possible sources of text posts to be analyzed using three text mining algorithms. We presented the three algorithms and the results of testing them in detecting different six intents. This work established that intent detection can be performed on text posts with approximately 61% accuracy.

On Demonstrating The Impact Of Defeasible Reasoning Via A Multi-Layer Argument-Based Framework (Doctoral Consortium), Lucas Middeldorf Rizzo

Conference papers

Promising results have indicated Argumentation Theory as a solid research area for implementing defeasible reasoning in practice. However, applications are usually domain dependent, not incorporating all the layers and steps required in an argumentation process, thus limit- ing their applicability in different areas. This PhD project is focused on the development of a multi-layer defeasible argument-based framework which is in turn used across different applications in the fields of decision making and knowledge representation and reasoning. The inference produced is compared against the inference of different quantitative theories of reasoning under uncertainty such as expert systems and fuzzy logic. The …

An Integrated Framework For Modeling And Predicting Spatiotemporal Phenomena In Urban Environments, Tuc Viet Le

Dissertations and Theses Collection (Open Access)

This thesis proposes a general solution framework that integrates methods in machine learning in creative ways to solve a diverse set of problems arising in urban environments. It particularly focuses on modeling spatiotemporal data for the purpose of predicting urban phenomena. Concretely, the framework is applied to solve three specific real-world problems: human mobility prediction, trac speed prediction and incident prediction. For human mobility prediction, I use visitor trajectories collected a large theme park in Singapore as a simplified microcosm of an urban area. A trajectory is an ordered sequence of attraction visits and corresponding timestamps produced by a visitor. …