Digital Commons Network^™

Anonymous Authentication Of Visitors For Mobile Crowd Sensing At Amusement Parks, Divyan Konidala, Robert H. Deng, Yingjiu Li, Hoong Chuin Lau, Stephen Fienberg

Research Collection School Of Computing and Information Systems

In this paper we focus on authentication and privacy aspects of an application scenario that utilizes mobile crowd sensing for the benefit of amusement park operators and their visitors. The scenario involves a mobile app that gathers visitors’ demographic details, preferences, and current location coordinates, and sends them to the park’s sever for various analyses. These analyses assist the park operators to efficiently deploy their resources, estimate waiting times and queue lengths, and understand the behavior of individual visitors and groups. The app server also offers visitors optimal recommendations on routes and attractions for an improved dynamic experience and minimized …

Master Physician Scheduling Problem, Aldy Gunawan, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We study a real-world problem arising from the operations of a hospital service provider, which we term the master physician scheduling problem. It is a planning problem of assigning physicians’ full range of day-to-day duties (including surgery, clinics, scopes, calls, administration) to the defined time slots/shifts over a time horizon, incorporating a large number of constraints and complex physician preferences. The goals are to satisfy as many physicians’ preferences and duty requirements as possible while ensuring optimum usage of available resources. We propose mathematical programming models that represent different variants of this problem. The models were tested on a real …

Enhancing Robot Perception Using Human Teammates, Jean Oh, Arne Suppe, Anthony Stentz, Martial Hebert

Research Collection School Of Computing and Information Systems

In robotics research, perception is one of the most challenging tasks. In contrast to existing approaches that rely only on computer vision, we propose an alternative method for improving perception by learning from human teammates. To evaluate, we apply this idea to a door detection problem. A set of preliminary experiments has been completed using software agents with real vision data. Our results demonstrate that information inferred from teammate observations significantly improves the perception precision.

Disclosing Climate Change Patterns Using An Adaptive Markov Chain Pattern Detection Method, Zhaoxia Wang, Gary Lee, Hoong Maeng Chan, Reuben Li, Xiuju Fu, Rick Goh, Pauline A. W. Poh Kim, Martin L. Hibberd, Hoong Chor Chin

Research Collection School Of Computing and Information Systems

This paper proposes an adaptive Markov chain pattern detection (AMCPD) method for disclosing the climate change patterns of Singapore through meteorological data mining. Meteorological variables, including daily mean temperature, mean dew point temperature, mean visibility, mean wind speed, maximum sustained wind speed, maximum temperature and minimum temperature are simultaneously considered for identifying climate change patterns in this study. The results depict various weather patterns from 1962 to 2011 in Singapore, based on the records of the Changi Meteorological Station. Different scenarios with varied cluster thresholds are employed for testing the sensitivity of the proposed method. The robustness of the proposed …

Why Individuals Seek Diverse Opinions (Or Why They Don't), Jisun An, Daniele Quercia, Jon Crowcroft

Research Collection School Of Computing and Information Systems

Fact checking has been hard enough to do in traditional settings, but, as news consumption is moving on the Internet and sources multiply, it is almost unmanageable. To solve this problem, researchers have created applications that expose people to diverse opinions and, as a result, expose them to balanced information. The wisdom of this solution is, however, placed in doubt by this paper. Survey responses of 60 individuals in the UK and South Korea and in-depth structured interviews of 10 respondents suggest that exposure to diverse opinions would not always work. That is partly because not all individuals equally value …

Distributed Gibbs: A Memory-Bounded Sampling-Based Dcop Algorithm, Duc Thien Nguyen, William Yeoh, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Researchers have used distributed constraint optimization problems (DCOPs) to model various multi-agent coordination and resource allocation problems. Very recently, Ottens et al. proposed a promising new approach to solve DCOPs that is based on confidence bounds via their Distributed UCT (DUCT) sampling-based algorithm. Unfortunately, its memory requirement per agent is exponential in the number of agents in the problem, which prohibits it from scaling up to large problems. Thus, in this paper, we introduce a new sampling-based DCOP algorithm called Distributed Gibbs, whose memory requirements per agent is linear in the number of agents in the problem. Additionally, we show …

Implementation Of Slowly Changing Dimension To Data Warehouse To Manage Marketing Campaigns In Banks, Lihui Wang, Junyu Choy, Michelle L. F. Cheong

Research Collection School Of Computing and Information Systems

Management of updating and recording campaign leads in data warehouse of any banking environment is complex especially with multiple campaigns are active simultaneously. As a way to avoid overly contacting customers for sales-based marketing contacts, the concept of Recency Frame is introduced to “lock” the customers who are targeted in Sales-based campaign for a specified time period. During this Recency Frame, the customer cannot be targeted by other Sales-based campaign under the same channel. This approach increased the difficulties of managing the customers’ data with proper data updating and storing and procedures have to be placed and made sufficiently robust …

Tesla: An Energy-Saving Agent That Leverages Schedule Flexibility, Jun Young Kwak, Pradeep Varakantham, Rajiv Maheswaran, Burcin Becerik-Gerber, Milind Tambe

Research Collection School Of Computing and Information Systems

This innovative application paper presents TESLA, an agent-based application for optimizing the energy use in commercial buildings. TESLA’s key insight is that adding flexibility to event/meeting schedules can lead to significant energy savings. TESLA provides three key contributions: (i) three online scheduling algorithms that consider flexibility of people’s preferences for energyefficient scheduling of incrementally/dynamically arriving meetings and events; (ii) an algorithm to effectively identify key meetings that lead to significant energy savings by adjusting their flexibility; and (iii) surveys of real users that indicate that TESLA’s assumptions exist in practice. TESLA was evaluated on data of over 110,000 meetings held …

Delayed Insertion And Rule Effect Moderation Of Domain Knowledge For Reinforcement Learning, Teck-Hou Teng, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Though not a fundamental pre-requisite to efficient machine learning, insertion of domain knowledge into adaptive virtual agent is nonetheless known to improve learning efficiency and reduce model complexity. Conventionally, domain knowledge is inserted prior to learning. Despite being effective, such approach may not always be feasible. Firstly, the effect of domain knowledge is assumed and can be inaccurate. Also, domain knowledge may not be available prior to learning. In addition, the insertion of domain knowledge can frame learning and hamper the discovery of more effective knowledge. Therefore, this work advances the use of domain knowledge by proposing to delay the …

Artificial Immune Systems And Particle Swarm Optimization For Solutions To The General Adversarial Agents Problem, Jeremy Mange

Dissertations

The general adversarial agents problem is an abstract problem description touching on the fields of Artificial Intelligence, machine learning, decision theory, and game theory. The goal of the problem is, given one or more mobile agents, each identified as either “friendly" or “enemy", along with a specified environment state, to choose an action or series of actions from all possible valid choices for the next “timestep" or series thereof, in order to lead toward a specified outcome or set of outcomes. This dissertation explores approaches to this problem utilizing Artificial Immune Systems, Particle Swarm Optimization, and hybrid approaches, along with …

Csc Senior Project: Nlpstats, Michael Mease

Computer Science and Software Engineering

Natural Language Processing has recently increased in popularity. The field of authorship analysis, specifically, uses various characteristics of text quantified by markers. NLPStats serves as a tool designed to streamline marker extraction based on user needs. A flexible query system allows for custom marker requests, adjustment of result formatting, and preprocessing options. Furthermore, an efficiently designed structure ensures that users retrieve information quickly. As a whole, NLPStats enables anyone, regardless of NLP experience, to extract important information about the text of a document.

Spoons: Netflix Outage Detection Using Microtext Classification, Eriq A. Augusitne

Master's Theses

Every week there are over a billion new posts to Twitter services and many of those messages contain feedback to companies about their services. One company that recognizes this unused source of information is Netflix. That is why Netflix initiated the development of a system that lets them respond to the millions of Twitter and Netflix users that are acting as sensors and reporting all types of user visible outages. This system enhances the feedback loop between Netflix and its customers by increasing the amount of customer feedback that Netflix receives and reducing the time it takes for Netflix to …

Joint Angle Tracking With Inertial Sensors, Mahmoud Ahmed El-Gohary

Dissertations and Theses

The need to characterize normal and pathological human movement has consistently driven researchers to develop new tracking devices and to improve movement analysis systems. Movement has traditionally been captured by either optical, magnetic, mechanical, structured light, or acoustic systems. All of these systems have inherent limitations. Optical systems are costly, require fixed cameras in a controlled environment, and suffer from problems of occlusion. Similarly, acoustic and structured light systems suffer from the occlusion problem. Magnetic and radio frequency systems suffer from electromagnetic disturbances, noise and multipath problems. Mechanical systems have physical constraints that limit the natural body movement. Recently, the …

Google And The World Brain, Dereck Daschke

Journal of Religion & Film

This is a film review of Google and the World Brain (2013) directed by Ben Lewis.

Object Detection And Recognition In Natural Settings, George William Dittmar

Dissertations and Theses

Much research as of late has focused on biologically inspired vision models that are based on our understanding of how the visual cortex processes information. One prominent example of such a system is HMAX [17]. HMAX attempts to simulate the biological process for object recognition in cortex based on the model proposed by Hubel & Wiesel [10]. This thesis investigates the ability of an HMAX-like system (GLIMPSE [20]) to perform object-detection in cluttered natural scenes. I evaluate these results using the StreetScenes database from MIT [1, 8]. This thesis addresses three questions: (1) Can the GLIMPSE-based object detection system replicate …

Towards Personalized Medicine Using Systems Biology And Machine Learning, Calin Voichita

Wayne State University Dissertations

The rate of acquiring biological data has greatly surpassed our ability to interpret it. At the same time, we have started to understand that evolution of many diseases such as cancer, are the results of the interplay between the disease itself and the immune system of the host. It is now well accepted that cancer is not a single disease, but a “complex collection of distinct genetic diseases united by common hallmarks”. Understanding the differences between such disease subtypes is key not only in providing adequate treatments for known subtypes but also identifying new ones. These unforeseen disease subtypes are …

Real-Time Control Of A Robot Arm Using An Inexpensive System For Electroencephalography Aided By Artificial Intelligence, James O'Connor

Computer Science Honors Papers

No abstract provided.

Reducing Communication Delay Variability For A Group Of Robots, Goncalo Martins

Electronic Theses and Dissertations

A novel architecture is presented for reducing communication delay variability for a group of robots. This architecture relies on using three components: a microprocessor architecture that allows deterministic real-time tasks; an event-based communication protocol in which nodes transmit in a TDMA fashion, without the need of global clock synchronization techniques; and a novel communication scheme that enables deterministic communications by allowing senders to transmit without regard for the state of the medium or coordination with other senders, and receivers can tease apart messages sent simultaneously with a high probability of success. This approach compared to others, allows simultaneous communications without …

Nonlinear Granger Causality And Its Application In Decoding Of Human Reaching Intentions, Mengting Liu

Doctoral Dissertations

Multi-electrode recording is a key technology that allows the brain mechanisms of decision making, cognition, and their breakdown in diseases to be studied from a network perspective. As the hypotheses concerning the role of neural interactions in cognitive paradigms become increasingly more elaborate, the ability to evaluate the direction of neural interactions in neural networks holds the key to distinguishing their functional significance.

Granger Causality (GC) is used to detect the directional influence of signals between multiple locations. To extract the nonlinear directional flow, GC was completed through a nonlinear predictive approach using radial basis functions (RBF). Furthermore, to obtain …

Using Mapreduce Streaming For Distributed Life Simulation On The Cloud, Atanas Radenski

Mathematics, Physics, and Computer Science Faculty Books and Book Chapters

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable …

Drift Detection Using Uncertainty Distribution Divergence, Patrick Lindstrom, Brian Mac Namee, Sarah Jane Delany

Articles

Data generated from naturally occurring processes tends to be non-stationary. For example, seasonal and gradual changes in climate data and sudden changes in financial data. In machine learning the degradation in classifier performance due to such changes in the data is known as concept drift and there are many approaches to detecting and handling it.

Most approaches to detecting concept drift, however, make the assumption that true classes for test examples will be available at no cost shortly after classification and base the detection of concept drift on measures relying on these labels. The high labelling cost in many domains …

Robotic Swarming Without Inter-Agent Communication, Daniel Jonathan Standish

USF Tampa Graduate Theses and Dissertations

Many physical and algorithmic swarms utilize inter-agent communication to achieve advanced swarming behaviors. These swarms are inspired by biological swarms that can be seen throughout nature and include bee swarms, ant colonies, fish schools, and bird flocks. These biological swarms do not utilize inter-agent communication like their physical and algorithmic counterparts. Instead, organisms in nature rely on a local awareness of other swarm members that facilitates proper swarm motion and behavior. This research aims to pursue an effective swarm algorithm using only line-of-sight proximity information and no inter-agent communication. It is expected that the swarm performance will be lower than …

Analyzing Environmental Change And Prehistoric Hunter Behavior Through A 3d Time-Lapsed Model With Level Auto-Generation And Cultural Algorithms, Samuel Dustin Stanley

Wayne State University Theses

This paper describes a system containing two portions whose purpose it is to help further the Alpena-Amberley Land Bridge research project and similar archaeological research. The first portion is a "time engine" which one can utilize to navigate through time in order to see how environmental conditions evolved as time passed, or to run experiments during a desired time period. The second portion is a hunting blind cultural algorithm, which is built on top of the time engine as well as Palazzolo's program. In this portion, the AI hunting blinds react to the goals that they are trying to achieve, …

Visual Semantic Segmentation And Its Applications, Jizhou Gao

Theses and Dissertations--Computer Science

This dissertation addresses the difficulties of semantic segmentation when dealing with an extensive collection of images and 3D point clouds. Due to the ubiquity of digital cameras that help capture the world around us, as well as the advanced scanning techniques that are able to record 3D replicas of real cities, the sheer amount of visual data available presents many opportunities for both academic research and industrial applications. But the mere quantity of data also poses a tremendous challenge. In particular, the problem of distilling useful information from such a large repository of visual data has attracted ongoing interests in …

Regret Based Robust Solutions For Uncertain Markov Decision Processes, Asrar Ahmed, Pradeep Reddy Varakantham, Yossiri Adulyasak, Patrick Jaillet

Research Collection School Of Computing and Information Systems

In this paper, we seek robust policies for uncertain Markov Decision Processes (MDPs). Most robust optimization approaches for these problems have focussed on the computation of maximin policies which maximize the value corresponding to the worst realization of the uncertainty. Recent work has proposed minimax regret as a suitable alternative to the maximin objective for robust optimization. However, existing algorithms for handling minimax regret are restricted to models with uncertainty over rewards only. We provide algorithms that employ sampling to improve across multiple dimensions: (a) Handle uncertainties over both transition and reward models; (b) Dependence of model uncertainties across state, …

Unmanned Autonomous Object Retrieval: Old Dominion University 2013 International Aerial Robotics Competition Entry, Johnathan Bailey, Austin Boyd, Chung-Hao Chen, Stephen Dailey, Lisa Henderson, Jeremy Stuart, Christina Williams

Electrical & Computer Engineering Faculty Publications

This paper describes the design implementation of a Quadrotor Unmanned Aerial Vehicle (UAV) with the capability of exploring indoor locations without the assistance of external aids. For relative position, the use of a laser range sensor, an optical flow sensor, and sonar sensor combined allows for the vehicle to generate mapping information. With relative position in mind, the vehicle uses vision algorithms to recognize immediate obstacles, sign, and entry ways to allow for quick movement responses and object recognition. A proportional-integral-differentiator controller allows for flight stability and mitigation in the tight confines of the indoor spaces. A mapping algorithm allows …

Introduction To Neutrosophic Measure, Neutrosophic Integral, And Neutrosophic Probability, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

In this book, we introduce for the first time the notions of neutrosophic measure and neutrosophic integral, and we develop the 1995 notion of neutrosophic probability. We present many practical examples.

It is possible to define the neutrosophic measure and consequently the neutrosophic integral and neutrosophic probability in many ways, because there are various types of indeterminacies, depending on the problem we need to solve. Neutrosophics study the indeterminacy. Indeterminacy is different from randomness. It can be caused by physical space materials and type of construction, by items involved in the space, etc.

Moving Object Detection With Laser Scanners, Christoph Mertz, Luis E. Navarro-Serment, Robert Maclachlan, Paul Rybski, Aaron Steinfeld, Arne Suppe, Christopher Urmson, Nicolas Vandapel, Martial Hebert, Chuck Thorpe, David Duggins, Jay Gowdy

Research Collection School Of Computing and Information Systems

The detection and tracking of moving objects is an essential task in robotics. The CMU-RI Navlab group has developed such a system that uses a laser scanner as its primary sensor. We will describe our algorithm and its use in several applications. Our system worked successfully on indoor and outdoor platforms and with several different kinds and configurations of two-dimensional and three-dimensional laser scanners. The applications vary from collision warning systems, people classification, observing human tracks, and input to a dynamic planner. Several of these systems were evaluated in live field tests and shown to be robust and reliable. (C) …

Decision Support For Assorted Populations In Uncertain And Congested Environments, Pradeep Reddy Varakantham, Asrar Ahmed, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

This research is motivated by large scale problems in urban transportation and labor mobility where there is congestion for resources and uncertainty in movement. In such domains, even though the individual agents do not have an identity of their own and do not explicitly interact with other agents, they effect other agents. While there has been much research in handling such implicit effects, it has primarily assumed deterministic movements of agents. We address the issue of decision support for individual agents that are identical and have involuntary movements in dynamic environments. For instance, in a taxi fleet serving a city, …

Security Games With Interval Uncertainty, Md Towhidul Islam

Open Access Theses & Dissertations

Game theory has become an important tool in solving real-life decision making problems. Security games use the concept of game theory in adversarial scenarios to protect critical infrastructure. The main purpose of security games is to allocate security resources among various targets and maximize payoff for the defender considering various kinds of attackers. It is hard for domain experts to predict the attacker's behavior, so one of the major challenges in describing this game model is representing uncertainty about the attacker's payoff. Several approaches have been developed to generate these game models based on uncertainty, such as Bayesian games. However …