Tools And Techniques For Computational Reproducibility,
2016
Brigham Young University
Tools And Techniques For Computational Reproducibility, Stephen Piccolo, Michael B. Frampton
Faculty Publications
When reporting research findings, scientists document the steps they followed so that others can verify and build upon the research. When those steps have been described in sufficient detail that others can retrace the steps and obtain similar results, the research is said to be reproducible. Computers play a vital role in many research disciplines and present both opportunities and challenges for reproducibility. Computers can be programmed to execute analysis tasks, and those programs can be repeated and shared with others. The deterministic nature of most computer programs means that the same analysis tasks, applied to the same data, will …
On Understanding Preference For Agile Methods Among Software Developers,
2016
Dakota State University
On Understanding Preference For Agile Methods Among Software Developers, David Brian Bishop, Amit V. Deokar, Surendra Sarnikar
Research & Publications
Agile methods are gaining widespread use in industry. Although management is keen on adopting agile, not all developers exhibit preference for agile methods. The literature is sparse in regard to why developers may show preference for agile. Understanding the factors informing the preference for agile can lead to more effective formation of teams, better training approaches, and optimizing software development efforts by focusing on key desirable components of agile. This study, using a grounded theory methodology, finds a variety of categories of factors that influence software developer preference for agile methods including self-efficacy, affective response, interpersonal response, external contingencies, and …
Gene Set Enrichment And Projection: A Computational Tool For Knowledge Discovery In Transcriptomes,
2016
Marquette University
Gene Set Enrichment And Projection: A Computational Tool For Knowledge Discovery In Transcriptomes, Karl Douglas Stamm
Dissertations (1934 -)
Explaining the mechanism behind a genetic disease involves two phases, collecting and analyzing data associated to the disease, then interpreting those data in the context of biological systems. The objective of this dissertation was to develop a method of integrating complementary datasets surrounding any single biological process, with the goal of presenting the response to a signal in terms of a set of downstream biological effects. This dissertation specifically tests the hypothesis that computational projection methods overlaid with domain expertise can direct research towards relevant systems-level signals underlying complex genetic disease. To this end, I developed a software algorithm named …
A Learning-To-Rank Based Fault Localization Approach Using Likely Invariants,
2016
Singapore Management University
A Learning-To-Rank Based Fault Localization Approach Using Likely Invariants, Tien-Duy B. Le, David Lo, Claire Le Goues, Lars Grunske
Research Collection School Of Computing and Information Systems
Debugging is a costly process that consumes much of developer time and energy. To help reduce debugging effort, many studies have proposed various fault localization approaches. These approaches take as input a set of test cases (some failing, some passing) and produce a ranked list of program elements that are likely to be the root cause of the failures (i.e., failing test cases). In this work, we propose Savant, a new fault localization approach that employs a learning-to-rank strategy, using likely invariant diffs and suspiciousness scores as features, to rank methods based on their likelihood to be a root cause …
Scalable Greedy Algorithms For Task/Resource Constrained Multi-Agent Stochastic Planning,
2016
Singapore Management University
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 …
Real-Time Salient Object Detection With A Minimum Spanning Tree,
2016
Singapore Management University
Real-Time Salient Object Detection With A Minimum Spanning Tree, Wei-Chih Tu, Shengfeng He, Qingxiong Yang, Shao-Yi Chien
Research Collection School Of Computing and Information Systems
In this paper, we present a real-time salient object detection system based on the minimum spanning tree. Due to the fact that background regions are typically connected to the image boundaries, salient objects can be extracted by computing the distances to the boundaries. However, measuring the image boundary connectivity efficiently is a challenging problem. Existing methods either rely on superpixel representation to reduce the processing units or approximate the distance transform. Instead, we propose an exact and iteration free solution on a minimum spanning tree. The minimum spanning tree representation of an image inherently reveals the object geometry information in …
Optimizing The Mix Of Games And Their Locations On The Casino Floor,
2016
nQube Technical Computing Corp.
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,
2016
nQube Technical Computing Corp.
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 …
Designing And Comparing Multiple Portfolios Of Parameter Configurations For Online Algorithm Selection,
2016
Singapore Management University
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 …
Robust Partial Order Schedules For Rcpsp/Max With Durational Uncertainty,
2016
Singapore Management University
Robust Partial Order Schedules For Rcpsp/Max With Durational Uncertainty, Na Fu, Pradeep Varakantham, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
In this work, we consider RCPSP/max with durational uncertainty. We focus on computing robust Partial Order Schedules (or, in short POS) which can be executed with risk controlled feasibility and optimality, i.e., there is stochastic posteriori quality guarantee that the derived POS can be executed with all constraints honored and completion before robust makespan. To address this problem, we propose BACCHUS: a solution method on Benders Accelerated Cut Creation for Handling Uncertainty in Scheduling. In our proposed approach, we first give an MILP formulation for the deterministic RCPSP/max and partition the model into POS generation process and start time schedule …
Packet Filter Approach To Detect Denial Of Service Attacks,
2016
California State University, San Bernardino
Packet Filter Approach To Detect Denial Of Service Attacks, Essa Yahya M Muharish
Electronic Theses, Projects, and Dissertations
Denial of service attacks (DoS) are a common threat to many online services. These attacks aim to overcome the availability of an online service with massive traffic from multiple sources. By spoofing legitimate users, an attacker floods a target system with a high quantity of packets or connections to crash its network resources, bandwidth, equipment, or servers. Packet filtering methods are the most known way to prevent these attacks via identifying and blocking the spoofed attack from reaching its target. In this project, the extent of the DoS attacks problem and attempts to prevent it are explored. The attacks categories …
Event Detection With Zero Example: Select The Right And Suppress The Wrong Concepts,
2016
Singapore Management University
Event Detection With Zero Example: Select The Right And Suppress The Wrong Concepts, Yi-Jie Lu, Hao Zhang, Maaike De Boer, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Complex video event detection without visual examples is a very challenging issue in multimedia retrieval. We present a state-of-the-art framework for event search without any need of exemplar videos and textual metadata in search corpus. To perform event search given only query words, the core of our framework is a large, pre-built bank of concept detectors which can understand the content of a video in the perspective of object, scene, action and activity concepts. Leveraging such knowledge can effectively narrow the semantic gap between textual query and the visual content of videos. Besides the large concept bank, this paper focuses …
Application Of Computer Modeling And Simulation Techniques For Optimization Of Factory Floor Operations In Small To Medium- Sized Businesses,
2016
Columbus State University
Application Of Computer Modeling And Simulation Techniques For Optimization Of Factory Floor Operations In Small To Medium- Sized Businesses, Brian P. Romano
Theses and Dissertations
The rationale and motive for this thesis was to prove that no matter the size of a company and its particular value stream, the application of applied computer science principles with a reliance on computer modeling and simulation onto the factory floor process improves efficiencies and throughput through the reduction of downtime and/or process waiting. This thesis research specifically emphasized small businesses of between $2 and $20 million and was purposely limited to factory floor production processes and utilized standardized applied computer science techniques including simulation and modeling, microprocessor based factory floor intelligence devices. The results of this applied technology …
The Contributions Of Anatol Rapoport To Game Theory,
2016
Western University
The Contributions Of Anatol Rapoport To Game Theory, Erika Simpson
Political Science Publications
Game theory is used to rationally and dispassionately examine the strategic behaviour of nations, especially superpower behaviour. This article explains how basic game theory - at its simplest level - was used by Anatol Rapoport to generate ideas about how to enhance world peace. Rapoport was at the forefront of the game theoreticians who sought to conceptualize strategies that could promote international cooperation. Accordingly, the basic logic of game theory is explained using the game models of ‘Chicken’ and ‘Prisoner’s Dilemma’. These models were used by Rapoport in his books and lectures in simple and complex ways. Then Rapoport’s revolutionary …
Ant Colony Optimization For Continuous Spaces,
2016
University of Arkansas, Fayetteville
Ant Colony Optimization For Continuous Spaces, Rachel Findley
Computer Science and Computer Engineering Undergraduate Honors Theses
Ant Colony Optimization (ACO) is an optimization algorithm designed to find semi-optimal solutions to Combinatorial Optimization Problems. The challenge of modifying this algorithm to effectively optimize over a continuous domain is one that has been tackled by several researchers. In this paper, ACO has been modified to use several variations of the algorithm for continuous spaces. An aspect of ACO which is crucial to its success when optimizing over a continuous space is choosing the appropriate object (solution component) out of an infinite set to add to the ant's path. This step is highly important in shaping good solutions. Important …
Online Sparse Passive Aggressive Learning With Kernels,
2016
Singapore Management University
Online Sparse Passive Aggressive Learning With Kernels, Jing Lu, Peilin Zhao, Hoi, Steven C. H.
Research Collection School Of Computing and Information Systems
Conventional online kernel methods often yield an unboundedlarge number of support vectors, making them inefficient and non-scalable forlarge-scale applications. Recent studies on bounded kernel-based onlinelearning have attempted to overcome this shortcoming. Although they can boundthe number of support vectors at each iteration, most of them fail to bound thenumber of support vectors for the final output solution which is often obtainedby averaging the series of solutions over all the iterations. In this paper, wepropose a novel kernel-based online learning method, Sparse Passive Aggressivelearning (SPA), which can output a final solution with a bounded number ofsupport vectors. The key idea of …
A Horizon Decomposition Approach For The Capacitated Lot-Sizing Problem With Setup Times,
2016
Singapore Management University
A Horizon Decomposition Approach For The Capacitated Lot-Sizing Problem With Setup Times, Ioannis Fragkos, Zeger Degraeve, Bert De Reyck
Research Collection Lee Kong Chian School Of Business
We introduce horizon decomposition in the context of Dantzig-Wolfe decomposition, and apply it to the capacitated lot-sizing problem with setup times. We partition the problem horizon in contiguous overlapping intervals and create subproblems identical to the original problem, but of smaller size. The user has the flexibility to regulate the size of the master problem and the subproblem via two scalar parameters. We investigate empirically which parameter configurations are efficient, and assess their robustness at different problem classes. Our branch-and-price algorithm outperforms state-of-the-art branch-and-cut solvers when tested to a new data set of challenging instances that we generated. Our methodology …
A Core Task Abstraction Approach To Hierarchical Reinforcement Learning [Extended Abstract],
2016
National University of Singapore
A Core Task Abstraction Approach To Hierarchical Reinforcement Learning [Extended Abstract], Zhuoru Li, Akshay Narayan, Tze-Yun Leong
Research Collection School Of Computing and Information Systems
We propose a new, core task abstraction (CTA) approach to learning the relevant transition functions in model-based hierarchical reinforcement learning. CTA exploits contextual independences of the state variables conditional on the task-specific actions; its promising performance is demonstrated through a set of benchmark problems.
Deeper Look Into Bug Fixes: Patterns, Replacements, Deletions, And Additions,
2016
Singapore Management University
Deeper Look Into Bug Fixes: Patterns, Replacements, Deletions, And Additions, Mauricio Soto, Ferdian Thung, Chu-Pan Wong, Claire Le Goues, David Lo
Research Collection School Of Computing and Information Systems
Many implementations of research techniques that automatically repair software bugs target programs written in C. Work that targets Java often begins from or compares to direct translations of such techniques to a Java context. However, Java and C are very different languages, and Java should be studied to inform the construction of repair approaches to target it. We conduct a large-scale study of bugfixing commits in Java projects, focusing on assumptions underlying common search-based repair approaches. We make observations that can be leveraged to guide high quality automatic software repair to target Java specifically, including common and uncommon statement modifications …
Sparse Feature Learning For Image Analysis In Segmentation, Classification, And Disease Diagnosis.,
2016
University of Louisville
Sparse Feature Learning For Image Analysis In Segmentation, Classification, And Disease Diagnosis., Ehsan Hosseini-Asl
Electronic Theses and Dissertations
The success of machine learning algorithms generally depends on intermediate data representation, called features that disentangle the hidden factors of variation in data. Moreover, machine learning models are required to be generalized, in order to reduce the specificity or bias toward the training dataset. Unsupervised feature learning is useful in taking advantage of large amount of unlabeled data, which is available to capture these variations. However, learned features are required to capture variational patterns in data space. In this dissertation, unsupervised feature learning with sparsity is investigated for sparse and local feature extraction with application to lung segmentation, interpretable deep …
