Computer Sciences Commons^™

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.

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.

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.

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 …

Outlier-Robust Tensor Pca, Pan Zhou, Jiashi Feng

Research Collection School Of Computing and Information Systems

Low-rank tensor analysis is important for various real applications in computer vision. However, existing methods focus on recovering a low-rank tensor contaminated by Gaussian or gross sparse noise and hence cannot effectively handle outliers that are common in practical tensor data. To solve this issue, we propose an outlier-robust tensor principle component analysis (OR-TPCA) method for simultaneous low-rank tensor recovery and outlier detection. For intrinsically low-rank tensor observations with arbitrary outlier corruption, OR-TPCA is the first method that has provable performance guarantee for exactly recovering the tensor subspace and detecting outliers under mild conditions. Since tensor data are naturally high-dimensional …

Sequential Decision Making For Improving Efficiency In Urban Environments, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Rapid "urbanization" (more than 50% of world's population now resides in cities) coupled with the natural lack of coordination in usage of common resources (ex: bikes, ambulances, taxis, traffic personnel, attractions) has a detrimental effect on a wide variety of response (ex: waiting times, response time for emergency needs) and coverage metrics (ex: predictability of traffic/security patrols) in cities of today. Motivated by the need to improve response and coverage metrics in urban environments, my research group is focussed on building intelligent agent systems that make sequential decisions to continuously match available supply of resources to an uncertain demand for …

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 …

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 …

Monte Carlo Approaches To Parameterized Poker Squares, Todd W. Neller, Zuozhi Yang, Colin M. Messinger, Calin Anton, Karo Castro-Wunsch, William Maga, Steven Bogaerts, Robert Arrington, Clay Langely

Computer Science Faculty Publications

The paper summarized a variety of Monte Carlo approaches employed in the top three performing entries to the Parameterized Poker Squares NSG Challenge competition. In all cases AI players benefited from real-time machine learning and various Monte Carlo game-tree search techniques.

Digital Integration, Jacob C. Boccio

USF Tampa Graduate Theses and Dissertations

Artificial intelligence is an emerging technology; something far beyond smartphones, cloud integration, or surgical microchip implantation. Utilizing the work of Ray Kurzweil, Nick Bostrom, and Steven Shaviro, this thesis investigates technology and artificial intelligence through the lens of the cinema. It does this by mapping contemporary concepts and the imagined worlds in film as an intersection of reality and fiction that examines issues of individual identity and alienation. I look at a non-linear timeline of films involving machine advancement, machine intelligence, and stages of post-human development; Elysium (2013) and Surrogates (2009) are about technology as an extension of the self, …

Energy Consumption Prediction With Big Data: Balancing Prediction Accuracy And Computational Resources, Katarina Grolinger, Miriam Am Capretz, Luke Seewald

Electrical and Computer Engineering Publications

In recent years, advances in sensor technologies and expansion of smart meters have resulted in massive growth of energy data sets. These Big Data have created new opportunities for energy prediction, but at the same time, they impose new challenges for traditional technologies. On the other hand, new approaches for handling and processing these Big Data have emerged, such as MapReduce, Spark, Storm, and Oxdata H2O. This paper explores how findings from machine learning with Big Data can benefit energy consumption prediction. An approach based on local learning with support vector regression (SVR) is presented. Although local learning itself is …

Analysis On Alergia Algorithm: Pattern Recognition By Automata Theory, Xuanyi Qi

Master's Projects

Based on Kolmogorov Complexity, a finite set x of strings has a pattern if the set x can be output by a Turing machine of length that is less than minimum of all |x|; this Turing machine, that may not be unique, is called a pattern of the finite set of string. In order to find a pattern of a given finite set of strings (assuming such a pattern exists), the ALERGIA algorithm is used to approximate such a pattern (Turing machine) in terms of finite automata. Note that each finite automaton defines a partition on formal language Σ*, ALERGIA …

Analyze Large Multidimensional Datasets Using Algebraic Topology, David Le

Master's Projects

This paper presents an efficient algorithm to extract knowledge from high-dimensionality, high- complexity datasets using algebraic topology, namely simplicial complexes. Based on concept of isomorphism of relations, our method turn a relational table into a geometric object (a simplicial complex is a polyhedron). So, conceptually association rule searching is turned into a geometric traversal problem. By leveraging on the core concepts behind Simplicial Complex, we use a new technique (in computer science) that improves the performance over existing methods and uses far less memory. It was designed and developed with a strong emphasis on scalability, reliability, and extensibility. This paper …

Dna Analysis Using Grammatical Inference, Cory Cook

Master's Projects

An accurate language definition capable of distinguishing between coding and non-coding DNA has important applications and analytical significance to the field of computational biology. The method proposed here uses positive sample grammatical inference and statistical information to infer languages for coding DNA.

An algorithm is proposed for the searching of an optimal subset of input sequences for the inference of regular grammars by optimizing a relevant accuracy metric. The algorithm does not guarantee the finding of the optimal subset; however, testing shows improvement in accuracy and performance over the basis algorithm.

Testing shows that the accuracy of inferred languages for …

Optimizing The Mix Of Games And Their Locations On The Casino Floor, Jason D. Fiege, Anastasia D. Baran

International Conference on Gambling & Risk Taking

We present a mathematical framework and computational approach that aims to optimize the mix and locations of slot machine types and denominations, plus other games to maximize the overall performance of the gaming floor. This problem belongs to a larger class of spatial resource optimization problems, concerned with optimizing the allocation and spatial distribution of finite resources, subject to various constraints. We introduce a powerful multi-objective evolutionary optimization and data-modelling platform, developed by the presenter since 2002, and show how this software can be used for casino floor optimization. We begin by extending a linear formulation of the casino floor …

Stationary And Time-Dependent Optimization Of The Casino Floor Slot Machine Mix, Anastasia D. Baran, Jason D. Fiege

International Conference on Gambling & Risk Taking

Modeling and optimizing the performance of a mix of slot machines on a gaming floor can be addressed at various levels of coarseness, and may or may not consider time-dependent trends. For example, a model might consider only time-averaged, aggregate data for all machines of a given type; time-dependent aggregate data; time-averaged data for individual machines; or fully time dependent data for individual machines. Fine-grained, time-dependent data for individual machines offers the most potential for detailed analysis and improvements to the casino floor performance, but also suffers the greatest amount of statistical noise. We present a theoretical analysis of single …

Machine Learning On The Cloud For Pattern Recognition, Tien Nguyen

Master's Projects

Pattern recognition is a field of machine learning with applications to areas such as text recognition and computer vision. Machine learning algorithms, such as convolutional neural networks, may be trained to classify images. However, such tasks may be computationally intensive for a commercial computer for larger volumes or larger sizes of images. Cloud computing allows one to overcome the processing and memory constraints of average commercial computers, allowing computations on larger amounts of data. In this project, we developed a system for detection and tracking of moving human and vehicle objects in videos in real time or near real time. …

Data-Driven Synthesis And Evaluation Of Syntactic Facial Expressions In American Sign Language Animation, Hernisa Kacorri

Dissertations, Theses, and Capstone Projects

Technology to automatically synthesize linguistically accurate and natural-looking animations of American Sign Language (ASL) would make it easier to add ASL content to websites and media, thereby increasing information accessibility for many people who are deaf and have low English literacy skills. State-of-art sign language animation tools focus mostly on accuracy of manual signs rather than on the facial expressions. We are investigating the synthesis of syntactic ASL facial expressions, which are grammatically required and essential to the meaning of sentences. In this thesis, we propose to: (1) explore the methodological aspects of evaluating sign language animations with facial expressions, …

Concatenative Synthesis For Novel Timbral Creation, James Eric Bilous

Master's Theses

Modern day musicians rely on a variety of instruments for musical expression. Tones produced from electronic instruments have become almost as commonplace as those produced by traditional ones as evidenced by the plethora of artists who can be found composing and performing with nothing more than a personal computer. This desire to embrace technical innovation as a means to augment performance art has created a budding field in computer science that explores the creation and manipulation of sound for artistic purposes. One facet of this new frontier concerns timbral creation, or the development of new sounds with unique characteristics that …

Supervised Learning For Multi-Domain Text Classification, Siva Charan Reddy Gangireddy

Master's Projects

Digital information available on the Internet is increasing day by day. As a result of this, the demand for tools that help people in finding and analyzing all these resources are also growing in number. Text Classification, in particular, has been very useful in managing the information. Text Classification is the process of assigning natural language text to one or more categories based on the content. It has many important applications in the real world. For example, finding the sentiment of the reviews, posted by people on restaurants, movies and other such things are all applications of Text classification. In …

Self-Organizing Neural Network For Adaptive Operator Selection In Evolutionary Search, Teck Hou Teng, Stephanus Daniel Handoko, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Evolutionary Algorithm is a well-known meta-heuristics paradigm capable of providing high-quality solutions to computationally hard problems. As with the other meta-heuristics, its performance is often attributed to appropriate design choices such as the choice of crossover operators and some other parameters. In this chapter, we propose a continuous state Markov Decision Process model to select crossover operators based on the states during evolutionary search. We propose to find the operator selection policy efficiently using a self-organizing neural network, which is trained offline using randomly selected training samples. The trained neural network is then verified on test instances not used for …

Multi Faceted Text Classification Using Supervised Machine Learning Models, Abhiteja Gajjala

Master's Projects

In recent year’s document management tasks (known as information retrieval) increased a lot due to availability of digital documents everywhere. The need of automatic methods for extracting document information became a prominent method for organizing information and knowledge discovery. Text Classification is one such solution, where in the natural language text is assigned to one or more predefined categories based on the content. In my research classification of text is mainly focused on sentiment label classification. The idea proposed for sentiment analysis is multi-class classification of online movie reviews. Many research papers discussed the classification of sentiment either positive or …

Designing And Comparing Multiple Portfolios Of Parameter Configurations For Online Algorithm Selection, Aldy Gunawan, Hoong Chuin Lau, Mustafa Misir

Research Collection School Of Computing and Information Systems

Algorithm portfolios seek to determine an effective set of algorithms that can be used within an algorithm selection framework to solve problems. A limited number of these portfolio studies focus on generating different versions of a target algorithm using different parameter configurations. In this paper, we employ a Design of Experiments (DOE) approach to determine a promising range of values for each parameter of an algorithm. These ranges are further processed to determine a portfolio of parameter configurations, which would be used within two online Algorithm Selection approaches for solving different instances of a given combinatorial optimization problem effectively. We …

Strategic Planning For Setting Up Base Stations In Emergency Medical Systems, Supriyo Ghosh, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Emergency Medical Systems (EMSs) are an important component of public health-care services. Improving infrastructure for EMS and specifically the construction of base stations at the ”right” locations to reduce response times is the main focus of this paper. This is a computationally challenging task because of the: (a) exponentially large action space arising from having to consider combinations of potential base locations, which themselves can be significant; and (b) direct impact on the performance of the ambulance allocation problem, where we decide allocation of ambulances to bases. We present an incremental greedy approach to discover the placement of bases that …

Dual Formulations For Optimizing Dec-Pomdp Controllers, Akshat Kumar, Hala Mostafa, Shlomo Zilberstein

Research Collection School Of Computing and Information Systems

Decentralized POMDP is an expressive model for multi-agent planning. Finite-state controllers (FSCs)---often used to represent policies for infinite-horizon problems---offer a compact, simple-to-execute policy representation. We exploit novel connections between optimizing decentralized FSCs and the dual linear program for MDPs. Consequently, we describe a dual mixed integer linear program (MIP) for optimizing deterministic FSCs. We exploit the Dec-POMDP structure to devise a compact MIP and formulate constraints that result in policies executable in partially-observable decentralized settings. We show analytically that the dual formulation can also be exploited within the expectation maximization (EM) framework to optimize stochastic FSCs. The resulting EM algorithm …

Exemplar-Driven Top-Down Saliency Detection Via Deep Association, Shengfeng He, Rynson W. H. Lau, Qingxiong Yang

Research Collection School Of Computing and Information Systems

Top-down saliency detection is a knowledge-driven search task. While some previous methods aim to learn this "knowledge" from category-specific data, others transfer existing annotations in a large dataset through appearance matching. In contrast, we propose in this paper a locateby-exemplar strategy. This approach is challenging, as we only use a few exemplars (up to 4) and the appearances among the query object and the exemplars can be very different. To address it, we design a two-stage deep model to learn the intra-class association between the exemplars and query objects. The first stage is for learning object-to-object association, and the second …

Serendipity-Driven Celebrity Video Hyperlinking, Shujun Yang, Lei Pang, Chong-Wah Ngo, Benoit Huet

Research Collection School Of Computing and Information Systems

This demo showcases the utility of video hyperlinks with celebrities as the link anchors and their social circles as targets, aiming to help users quickly explore the aboutness of a celebrity by link traversal. Through content analysis, our system embeds hyperlinks into videos such that users can click-and-jump between celebrity faces in different videos to get-to-know their social circles. One peculiar feature is the ability of the system in providing links that maximize users' chance encounter, or serendipitous experience, beyond information need. Our system is enabled by two key components, name-face association and diversity-based ranking, for the aboutness and serendipity …