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Intelligent And Human-Aware Decision Making For Semi-Autonomous Human Rehabilitation Assistance Using Modular Robots, Anoop Mishra 2018 University of Nebraska at Omaha

Intelligent And Human-Aware Decision Making For Semi-Autonomous Human Rehabilitation Assistance Using Modular Robots, Anoop Mishra

Student Research and Creative Activity Fair

Modular Self-reconfigurable Robots (MSRs) are robots that can adapt their shape and mobility while performing their operations. We are developing an MSR called MARIO (Modular Robots for Assistance in Robust and Intelligent Operations) to assist patients with spinal cord injury in performing daily living tasks. In this research, we are investigating computational techniques that will enable MARIO to autonomously adapt its shape while performing an assistive task, and, while remaining aware of the human user’s satisfaction in receiving assistance from MARIO. We are developing semi-autonomous decision making techniques within a computational framework called shared autonomy that will adapt MARIO ...


The Threat Of Artificial Superintelligence, Joseph D. Ebhardt 2018 Lord Fairfax Community College

The Threat Of Artificial Superintelligence, Joseph D. Ebhardt

Exigence

This paper discusses the development of AI and the threat posed by the theoretical achievement of artificial superintelligence. AI is becoming an increasingly significant fixture in our lives and this will only continue in the future. The development of artificial general intelligence (AGI) would quickly lead to artificial superintelligence (ASI). AI researcher Steve Omohundro’s universal drives of rational systems demonstrate why ASI could behave in ways unanticipated by its designers. A technological singularity may occur if AI is allowed to undergo uncontrolled rapid self-improvement, which could pose an extinction-level risk to the human race. Two possible safety measures, AI ...


Vision-Based Assistive Indoor Localization, Feng Hu 2018 The Graduate Center, City University of New York

Vision-Based Assistive Indoor Localization, Feng Hu

All Dissertations, Theses, and Capstone Projects

An indoor localization system is of significant importance to the visually impaired in their daily lives by helping them localize themselves and further navigate an indoor environment. In this thesis, a vision-based indoor localization solution is proposed and studied with algorithms and their implementations by maximizing the usage of the visual information surrounding the users for an optimal localization from multiple stages. The contributions of the work include the following: (1) Novel combinations of a daily-used smart phone with a low-cost lens (GoPano) are used to provide an economic, portable, and robust indoor localization service for visually impaired people. (2 ...


Object Localization, Segmentation, And Classification In 3d Images, Allan Zelener 2018 The Graduate Center, City University of New York

Object Localization, Segmentation, And Classification In 3d Images, Allan Zelener

All Dissertations, Theses, and Capstone Projects

We address the problem of identifying objects of interest in 3D images as a set of related tasks involving localization of objects within a scene, segmentation of observed object instances from other scene elements, classifying detected objects into semantic categories, and estimating the 3D pose of detected objects within the scene. The increasing availability of 3D sensors motivates us to leverage large amounts of 3D data to train machine learning models to address these tasks in 3D images. Leveraging recent advances in deep learning has allowed us to develop models capable of addressing these tasks and optimizing these tasks jointly ...


Multimodal Sensing And Data Processing For Speaker And Emotion Recognition Using Deep Learning Models With Audio, Video And Biomedical Sensors, Farnaz Abtahi 2018 The Graduate Center, City University of New York

Multimodal Sensing And Data Processing For Speaker And Emotion Recognition Using Deep Learning Models With Audio, Video And Biomedical Sensors, Farnaz Abtahi

All Dissertations, Theses, and Capstone Projects

The focus of the thesis is on Deep Learning methods and their applications on multimodal data, with a potential to explore the associations between modalities and replace missing and corrupt ones if necessary. We have chosen two important real-world applications that need to deal with multimodal data: 1) Speaker recognition and identification; 2) Facial expression recognition and emotion detection.

The first part of our work assesses the effectiveness of speech-related sensory data modalities and their combinations in speaker recognition using deep learning models. First, the role of electromyography (EMG) is highlighted as a unique biometric sensor in improving audio-visual speaker ...


Feature Based Calibration Of A Network Of Kinect Sensors, Xiaoyang Li 2018 The University of Western Ontario

Feature Based Calibration Of A Network Of Kinect Sensors, Xiaoyang Li

Electronic Thesis and Dissertation Repository

The availability of affordable depth sensors in conjunction with common RGB cameras, such as the Microsoft Kinect, can provide robots with a complete and instantaneous representation of the current surrounding environment. However, in the problem of calibrating multiple camera systems, traditional methods bear some drawbacks, such as requiring human intervention. In this thesis, we propose an automatic and reliable calibration framework that can easily estimate the extrinsic parameters of a Kinect sensor network. Our framework includes feature extraction, Random Sample Consensus and camera pose estimation from high accuracy correspondences. We also implement a robustness analysis of position estimation algorithms. The ...


Quantifying The Trade-Off Between Two Types Of Morphological Complexity, Ryan Cotterell, Christo Kirov, Mans Hulden, Jason Eisner 2018 Johns Hopkins University

Quantifying The Trade-Off Between Two Types Of Morphological Complexity, Ryan Cotterell, Christo Kirov, Mans Hulden, Jason Eisner

Proceedings of the Society for Computation in Linguistics

No abstract provided.


Bibliography For Interstices 2018: Beyond Human: Emotion And Ai, Kristin Laughtin-Dunker 2018 Chapman University

Bibliography For Interstices 2018: Beyond Human: Emotion And Ai, Kristin Laughtin-Dunker

Library Displays and Bibliographies

An annotated list of materials in the Leatherby Libraries to accompany the Interstices 2018: Beyond Human: Emotion and AI event held at Chapman University in February 2018. The event featured Lisa Joy, co-creator and executive producer of HBO’s Emmy winning hit series Westworld, Jon Gratch, Director for Virtual Human Research at the University of Southern California’s (USC) Institute for Creative Technologies and Caroline Bainbridge, a Professor of Psychoanalysis and Culture in the Department of Media, Culture and Language at the University of Roehampton London. The Leatherby Libraries also hosted two book club discussions of The Positronic Man by ...


Smu Master Of It In Business Launches New Artificial Intelligence Track, Singapore Management University 2018 Singapore Management University

Smu Master Of It In Business Launches New Artificial Intelligence Track, Singapore Management University

SMU Press Releases

The Singapore Management University’s School of Information Systems (SIS) has launched a new Artificial Intelligence (AI) track under its Master of IT in Business (MITB) programme. Geared towards nurturing graduates who are ready for the revolutionary change from AI in data science, the AI track equips a new generation of IT business leaders in careers that bridge AI with business.


Pricing For A Last-Mile Transportation System, Hai WANG, Yiwei CHEN 2018 Singapore Management University

Pricing For A Last-Mile Transportation System, Hai Wang, Yiwei Chen

Research Collection School Of Information Systems

The Last-Mile Problem refers to the provision of travel service from the nearest public transportation node to a home or other destination. Last-Mile Transportation System (LMTS), which has recently emerged, provide on-demand shared transportation. We consider an LMTS with multiple passenger types—adults, senior citizens, children, and students. The LMTS designer determines the price for the passengers, last-mile service vehicle capacity, and service fleet size (number of vehicles) for each last-mile region to maximize the social welfare generated by the LMTS. The level of last-mile service (in terms of passenger waiting time) is approximated by using a batch arrival, batch ...


Visual Odometry Using Convolutional Neural Networks, Alec Graves, Steffen Lim, Thomas Fagan, Kevin McFall PhD. 2017 Kennesaw State University

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 2017 University of New Orleans, New Orleans

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 ...


An Unmanned Aerial System For Prescribed Fires, Evan M. Beachly 2017 University of Nebraska-Lincoln

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

Computer Science and Engineering: Theses, Dissertations, 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 ...


Analyzing Political Bias Through A User-Friendly Interface, Colin Lightfoot 2017 College of William and Mary

Analyzing Political Bias Through A User-Friendly Interface, Colin Lightfoot

Undergraduate Honors Theses

Many news outlets report stories shown with biased undertones that mislead readers to believe one story over another about the same event. To help people delineate between liberallyand conservatively-biased news articles, we created a website which uses a recurrent neural network with long short-term memory nodes trained to identify bias found in news articles. The network achieved an F1 Score of 0.76, and is used to provide one liberally-biased article and one conservatively-biased article side-by-side for a user to read when the user searches for a specific news story


Authorship Identification Of Translation Algorithms., Keishin Nishiyama 2017 University of Louisville

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 ...


Policy Gradient With Value Function Approximation For Collective Multiagent Planning, Duc Thien NGUYEN, Akshat KUMAR, Hoong Chuin LAU 2017 Singapore Management University

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

Research Collection School Of 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 ...


Scalable Urban Mobile Crowdsourcing: Handling Uncertainty In Worker Movement, Shih-Fen CHENG, CHEN CEN, Thivya KANDAPPU, Hoong Chuin LAU, Archan MISRA, Nikita JAIMAN, Randy Tandriansyah DARATAN, Ming Hui, Desmond (XU Minghui) KOH 2017 Singapore Management University

Scalable Urban Mobile Crowdsourcing: Handling Uncertainty In Worker Movement, Shih-Fen Cheng, Chen Cen, Thivya Kandappu, Hoong Chuin Lau, Archan Misra, Nikita Jaiman, Randy Tandriansyah Daratan, Ming Hui, Desmond (Xu Minghui) Koh

Research Collection School Of Information Systems

In this article, we investigate effective ways of utilizing crowdworkers in providing various urban services. The task recommendation platform that we design can match tasks to crowdworkers based on workers’ historical trajectories and time budget limits, thus making recommendations personal and efficient. One major challenge we manage to address is the handling of crowdworker’s trajectory uncertainties. In this article, we explicitly allow multiple routine routes to be probabilistically associated with each worker. We formulate this problem as an integer linear program whose goal is to maximize the expected total utility achieved by all workers. We further exploit the separable ...


Efficient Gate System Operations For A Multi-Purpose Port Using Simulation Optimization, Ketki KULKARNI, Khiem Trong TRAN, Hai WANG, Hoong Chuin LAU 2017 Singapore Management University

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

Research Collection School Of 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 ...


Ethics And Bias In Machine Learning: A Technical Study Of What Makes Us “Good”, Ashley Nicole Shadowen 2017 CUNY John Jay College of Criminal Justice

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 ...


Nbpmf: Novel Network-Based Inference Methods For Peptide Mass Fingerprinting, Zhewei Liang 2017 The University of Western Ontario

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 ...


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