Artificial Intelligence and Robotics Commons^™

Towards A Deep Learning-Based Activity Discovery System, Eoin Rogers, John D. Kelleher, Robert J. Ross

Conference papers

Activity discovery is a challenging machine learning problem where we seek to uncover new or altered behavioural patterns in sensor data. In this paper we motivate and introduce a novel approach to activity discovery based on modern deep learning techniques. We hypothesise that our proposed approach can deal with interleaved datasets in a more intelligent manner than most existing AD methods. We also build upon prior work building hierarchies of activities that capture the inherent ag- gregate nature of complex activities and show how this could plausibly be adapted to work with the deep learning technique we present. Finally, we …

An Intelligent System For Personalized Conference Event Recommendation And Scheduling, Aldy Gunawan, Hoong Chuin Lau, Pradeep Varakantham, Wenjie Wang

Research Collection School Of Computing and Information Systems

Many conference mobile apps today lack the intelligent feature to automatically generates optimal schedules based on delegates' preferences. This entails two major challenges: (a) identifying preferences of users; and (b) given the preferences, generating a schedule that optimizes his preferences. In this paper, we specifically focus on academic conferences, where users are prompted to input their preferred keywords. Our key contribution is an integrated conference scheduling agent that automatically recognizes user preferences based on keywords, provides a list of recommended talks and optimizes user schedule based on these preferences. To demonstrate the utility of our integrated conference scheduling agent, we …

Tasker: Behavioral Insights Via Campus-Based Experimental Mobile Crowd-Sourcing, Thivya Kandappu, Nikita Jaiman, Randy Tandriansyah Daratan, Archan Misra, Shih-Fen Cheng, Cen Chen, Hoong Chuin Lau, Deepthi Chander, Koustuv Dasgupta

Research Collection School Of Computing and Information Systems

While mobile crowd-sourcing has become a game-changer for many urban operations, such as last mile logistics and municipal monitoring, we believe that the design of such crowdsourcing strategies must better accommodate the real-world behavioral preferences and characteristics of users. To provide a real-world testbed to study the impact of novel mobile crowd-sourcing strategies, we have designed, developed and experimented with a real-world mobile crowd-tasking platform on the SMU campus, called TA$Ker. We enhanced the TA$Ker platform to support several new features (e.g., task bundling, differential pricing and cheating analytics) and experimentally investigated these features via a two-month deployment of TA$Ker, …

Important Considerations For Human Activity Recognition Using Sensor Data, Matt Buckner

Rose-Hulman Undergraduate Research Publications

Automated human activity recognition has received much attention in recent years due to increasing focus on interconnected devices in The Internet of Things (IoT) and the miniaturization and proliferation of sensor systems with the adoption of smartphones. In this work, we focus on the current status of human activity recognition across multiple studies, including methodology, accuracy of results, and current challenges to implementation. We include some preliminary work we have completed on a sensor system for classifying treadmill usage.

Real Time Activity Recognition Of Treadmill Usage Via Machine Learning, Nathan Blank, Matt Buckner, Christian Owen, Anna Scott

Rose-Hulman Undergraduate Research Publications

Our objective is to provide real-time classification of treadmill usage patterns based on accelerometer and magnetometer measurements. We collected data from treadmills in the Rose-Hulman Student Recreation Center (SRC) using Shimmer3 sensor units. We identified useful data features and classifiers for predicting treadmill usage patterns. We also prototyped a proof of concept wireless, real-time classification system.

Toward Autonomous Multi-Rotor Indoor Aerial Vehicles, Connor Brooks

Mahurin Honors College Capstone Experience/Thesis Projects

In this project, we worked to create an indoor autonomous micro aerial vehicle (MAV) using a multi-layer architecture with modular hardware and software components. We required that all computing was done onboard the vehicle during time of flight so that no remote connection of any kind was necessary for successful control of the vehicle, even when flying autonomously. We utilized environmental sensors including ultrasonic sensors, light detection and ranging modules, and inertial measurement units to acquire necessary environment information for autonomous flight. We used a three-layered system that combined a modular control architecture with distributed on-board computing to allow for …

Classifying Pattern Formation In Materials Via Machine Learning, Lukasz Burzawa, Shuo Liu, Erica W. Carlson

The Summer Undergraduate Research Fellowship (SURF) Symposium

Scanning probe experiments such as scanning tunneling microscopy (STM) and atomic force microscopy (AFM) on strongly correlated materials often reveal complex pattern formation that occurs on multiple length scales. We have shown in two disparate correlated materials that the pattern formation is driven by proximity to a disorder-driven critical point. We developed new analysis concepts and techniques that relate the observed pattern formation to critical exponents by analyzing the geometry and statistics of clusters observed in these experiments and converting that information into critical exponents. Machine learning algorithms can be helpful correlating data from scanning probe experiments to theoretical models …

Robust Algorithms For Estimating Vehicle Movement From Motion Sensors Within Smartphones, Ilyas Ustun

Computational Modeling & Simulation Engineering Theses & Dissertations

Building sustainable traffic control solutions for urban streets (e.g., eco-friendly signal control) and highways requires effective and reliable sensing capabilities for monitoring traffic flow conditions so that both the temporal and spatial extents of congestion are observed. This would enable optimal control strategies to be implemented for maximizing efficiency and for minimizing the environmental impacts of traffic. Various types of traffic detection systems, such as inductive loops, radar, and cameras have been used for these purposes. However, these systems are limited, both in scope and in time. Using GPS as an alternative method is not always viable because of problems …

Interactive Logical Analysis Of Planning Domains, Rajesh Kalyanam

Open Access Dissertations

Humans exhibit a significant ability to answer a wide range of questions about previously unencountered planning domains, and leverage this ability to construct “general-purpose'' solution plans for the domain.

The long term vision of this research is to automate this ability, constructing a system that utilizes reasoning to automatically verify claims about a planning domain. The system would use this ability to automatically construct and verify a generalized plan to solve any planning problem in the domain. The goal of this thesis is to start with baseline results from the interactive verification of claims about planning domains and develop the …

Data Driven Low-Bandwidth Intelligent Control Of A Jet Engine Combustor, Nathan L. Toner

Open Access Dissertations

This thesis introduces a low-bandwidth control architecture for navigating the input space of an un-modeled combustor system between desired operating conditions while avoiding regions of instability and blow-out. An experimental procedure is discussed for identifying regions of instability and gathering sufficient data to build a data-driven model of the system's operating modes. Regions of instability and blow-out are identified experimentally and a data-driven operating point classifier is designed. This classifier acts as a map of the operating space of the combustor, indicating regions in which the flame is in a "good" or "bad" operating mode. A data-driven predictor is also …

New Developments In Metaheuristics And Their Applications: Selected Extended Contributions From The 10th Metaheuristics International Conference (Mic 2013), Hoong Chuin Lau, Günther R. Raidl, Pascal Van Hentenryck

Research Collection School Of Computing and Information Systems

No abstract provided.

A Fast Algorithm For Personalized Travel Planning Recommendation, Aldy Gunawan, Hoong Chuin Lau, Kun Lu

Research Collection School Of Computing and Information Systems

With the pervasive use of recommender systems and web/mobile applications such as TripAdvisor and Booking.com, an emerging interest is to generate personalized tourist routes based on a tourist’s preferences and time budget constraints, often in real-time. The problem is generally known as the Tourist Trip Design Problem (TTDP) which is a route-planning problem on multiple Points of Interest (POIs). TTDP can be considered as an extension of the classical problem of Team Orienteering Problem with Time Windows (TOPTW). The objective of the TOPTW is to determine a fixed number of routes that maximize the total collected score. The TOPTW also …

Enhancing Local Search With Adaptive Operator Ordering And Its Application To The Time Dependent Orienteering Problem, Aldy Gunawan, Hoong Chuin Lau, Kun Lu

Research Collection School Of Computing and Information Systems

No abstract provided.

Artificial Intelligence And Amikacin Exposures Predictive Of Outcomes In Multidrug-Resistant Tuberculosis Patients, Chawangwa Modongo, Jotam G. Pasipanodya, Shashikant Srivastava, Nicola Zetola, Scott Williams, Giorgio Sirugo, Tawanda Gumbo

Dartmouth Scholarship

Aminoglycosides such as amikacin continue to be part of the backbone of treatment of multidrug-resistant tuberculosis (MDR- TB). We measured amikacin concentrations in 28 MDR-TB patients in Botswana receiving amikacin therapy together with oral levofloxacin, ethionamide, cycloserine, and pyrazinamide and calculated areas under the concentration-time curves from 0 to 24 h (AUC0 –24). The patients were followed monthly for sputum culture conversion based on liquid cultures. The median duration of amikacin therapy was 184 (range, 28 to 866) days, at a median dose of 17.30 (range 11.11 to 19.23) mg/kg. Only 11 (39%) pa- tients had sputum culture conversion during …

Vision-Based Motion For A Humanoid Robot, Khalid Abdullah Alkhulayfi

Dissertations and Theses

The overall objective of this thesis is to build an integrated, inexpensive, human-sized humanoid robot from scratch that looks and behaves like a human. More specifically, my goal is to build an android robot called Marie Curie robot that can act like a human actor in the Portland Cyber Theater in the play Quantum Debate with a known script of every robot behavior. In order to achieve this goal, the humanoid robot need to has degrees of freedom (DOF) similar to human DOFs. Each part of the Curie robot was built to achieve the goal of building a complete humanoid …

A Genetic Algorithmic Approach To Automated Auction Mechanism Design, Jinzhong Niu, Simon Parsons

Publications and Research

In this paper, we present a genetic algorithmic approach to automated auction mechanism design in the context of \cat games. This is a follow-up to one piece of our prior work in the domain, the reinforcement learning-based grey-box approach. Our experiments show that given the same search space the grey-box approach is able to produce better auction mechanisms than the genetic algorithmic approach. The comparison can also shed light on the design and evaluation of similar search solutions to other domain problems.

Robust Repositioning To Counter Unpredictable Demand In Bike Sharing Systems, Supriyo Ghosh, Michael Trick, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Bike Sharing Systems (BSSs) experience a significant loss in customer demand due to starvation (empty base stations precluding bike pickup) or congestion (full base stations precluding bike return). Therefore, BSSs operators reposition bikes between stations with the help of carrier vehicles. Due to unpredictable and dynamically changing nature of the demand, myopic reasoning typically provides a below par performance. We propose an online and robust repositioning approach to minimise the loss in customer demand while considering the possible uncertainty in future demand. Specifically, we develop a scenario generation approach based on an iterative two player game to compute a strategy …

Constraint Answer Set Programming Versus Satisfiability Modulo Theories, Yuliya Lierler, Benjamin Susman

Computer Science Faculty Proceedings & Presentations

Constraint answer set programming is a promising research direction that integrates answer set programming with constraint processing. It is often informally related to the field of Satisfiability Modulo Theories. Yet, the exact formal link is obscured as the terminology and concepts used in these two research areas differ. In this paper, we make the link between these two areas precise.

A Hybrid Tabu/Scatter Search Algorithm For Simulation-Based Optimization Of Multi-Objective Runway Operations Scheduling, Bulent Soykan

Engineering Management & Systems Engineering Theses & Dissertations

As air traffic continues to increase, air traffic flow management is becoming more challenging to effectively and efficiently utilize airport capacity without compromising safety, environmental and economic requirements. Since runways are often the primary limiting factor in airport capacity, runway operations scheduling emerge as an important problem to be solved to alleviate flight delays and air traffic congestion while reducing unnecessary fuel consumption and negative environmental impacts. However, even a moderately sized real-life runway operations scheduling problem tends to be too complex to be solved by analytical methods, where all mathematical models for this problem belong to the complexity class …

Scalable Greedy Algorithms For Task/Resource Constrained Multi-Agent Stochastic Planning, Pritee Agrawal, Pradeep Varakantham, William Yeoh

Research Collection School Of Computing and Information Systems

Synergistic interactions between task/resource allocation and stochastic planning exist in many environments such as transportation and logistics, UAV task assignment and disaster rescue. Existing research in exploiting these synergistic interactions between the two problems have either only considered domains where tasks/resources are completely independent of each other or have focussed on approaches with limited scalability. In this paper, we address these two limitations by introducing a generic model for task/resource constrained multi-agent stochastic planning, referred to as TasC-MDPs. We provide two scalable greedy algorithms, one of which provides posterior quality guarantees. Finally, we illustrate the high scalability and solution performance …