Theory and Algorithms Commons^™

Data-Driven Investment Decisions In P2p Lending: Strategies Of Integrating Credit Scoring And Profit Scoring, Yan Wang

Doctor of Data Science and Analytics Dissertations

In this dissertation, we develop and discuss several loan evaluation methods to guide the investment decisions for peer-to-peer (P2P) lending. In evaluating loans, credit scoring and profit scoring are the two widely utilized approaches. Credit scoring aims at minimizing the risk while profit scoring aims at maximizing the profit. This dissertation addresses the strengths and weaknesses of each scoring method by integrating them in various ways in order to provide the optimal investment suggestions for different investors. Before developing the methods for loan evaluation at the individual level, we applied the state-of-the-art method called the Long Short Term Memory (LSTM) …

Quantum Computing: Principles And Applications, Yoshito Kanamori, Seong-Moo Yoo

Journal of International Technology and Information Management

The development of quantum computers over the past few years is probably one of the significant advancements in the history of quantum computing. D-Wave quantum computer has been available for more than eight years. IBM has made its quantum computer accessible via its cloud service. Also, Microsoft, Google, Intel, and NASA have been heavily investing in the development of quantum computers and their applications. The quantum computer seems to be no longer just for physicists and computer scientists but also for information system researchers. This paper introduces the basic concepts of quantum computing and describes well-known quantum applications for non-physicists. …

Identifying Relationships Of Interest In Complex Environments By Using Channel Theory, Andreas Bildstein, Junkang Feng

Communications of the IIMA

Complex environments show a high degree of dynamics caused by vital interactions between objects within those environments and alterations through which the set of objects and their characteristics within those environments go over time. Within this work, we show that we can tame the level of complexity in dynamic environments by identifying ‘relationships of interest’ between objects in such environments. To this end, we apply the theory of Information Flow, also known as Channel Theory, to the application area of smart manufacturing. We enhance the way how the Channel Theory has been applied so far by using an …

A Common Approach For Consumer And Provider Fairness In Recommendations, Dimitris Sacharidis, Kyriakos Mouratidis, Dimitrios Kleftogiannis

Research Collection School Of Computing and Information Systems

We present a common approach for handling consumer and provider fairness in recommendations. Our solution requires defining two key components, a classification of items and a target distribution, which together define the case of perfect fairness. This formulation allows distinct fairness concepts to be specified in a common framework. We further propose a novel reranking algorithm that optimizes for a desired trade-off between utility and fairness of a recommendation list.

Tools To Improve Interruption Management, Matthew R. Munns

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

Interruptions carry a high cost, especially to software developers. To prevent unnecessary interruptions, several technologies are being explored that can help manage the timing of interruptions, such as displaying the interruptibility of a worker to their peers. Relatively simple algorithms utilizing computer interaction data have been created and used successfully in the workplace, while technology using bio-metric emotion recognition to detect the interruptibility of a user is also being developed.

Can Algorithms Help Us Decide Who To Trust?, David De Cremer, Jack Mcguire, Yorck Hesselbarth, Ke M Mai

Research Collection Lee Kong Chian School Of Business

The use of artificial intelligence (AI) and algorithms is increasing within organizations to manage business processes, hire employees, and automate routine organizational decision making. This comes as no surprise, since the application of simple linear algorithms have been shown to outperform human judgment in the accuracy of many administrative tasks. A 2017 Accenture survey also revealed that 85% of executives want to invest more extensively in AI-related technologies over the next three years.

Use Of Exploratory Data Analysis And Visualization In Identification Of Commingled Human Remains, Vivek Bhat Hosmat

UNO Student Research and Creative Activity Fair

The field of Exploratory data analysis and Visualization is revolutionizing the way we perceive things. At times, Visualization is seen as a subset of Exploratory data analysis and then there are branches in Visualization, types, technologies, tools which makes one feel that Visualization is a standalone pillar. Either way, they both work well together to extract meaningful information from almost any kind of data. These fields can very well be the stepping stone for in-depth or conclusive research. They can direct research based on facts and observations rather than one’s intuition or even a brute force technique. This project was …

Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater

SMU Data Science Review

The problem of forecasting market volatility is a difficult task for most fund managers. Volatility forecasts are used for risk management, alpha (risk) trading, and the reduction of trading friction. Improving the forecasts of future market volatility assists fund managers in adding or reducing risk in their portfolios as well as in increasing hedges to protect their portfolios in anticipation of a market sell-off event. Our analysis compares three existing financial models that forecast future market volatility using the Chicago Board Options Exchange Volatility Index (VIX) to six machine/deep learning supervised regression methods. This analysis determines which models provide best …

Agent-Based Modeling And Simulation Approaches In Stem Education Research, Shanna R. Simpson-Singleton, Xiangdong Che

Journal of International Technology and Information Management

The development of best practices that deliver quality STEM education to all students, while minimizing achievement gaps, have been solicited by several national agencies. ABMS is a feasible approach to provide insight into global behavior based upon the interactions amongst agents and environments. In this review, we systematically surveyed several modeling and simulation approaches and discussed their applications to the evaluation of relevant theories in STEM education. It was found that ABMS is optimal to simulate STEM education hypotheses, as ABMS will sensibly present emergent theories and causation in STEM education phenomena if the model is properly validated and calibrated.

Marine Quay Crane Scheduling Using A Combined Modified Genetic Algorithm And Priority Rules Approach, V. H. Nguyen, D. T. Nguyen

Civil & Environmental Engineering Faculty Publications

Quay crane scheduling problem (QCSP) is the problem of the allocation of quay cranes to handle the unloading and loading of containers at seaport container terminals and defining the service sequence of vessel bays of each quay crane. The treatment of crane interference constraints and the increased in vessel size make the problem difficult to solve. Due to the growing interest in applied research for this problem, many researchers have used different algorithms and methods to obtain some solutions. This paper will propose a modified genetic algorithm combined with priority rules to deal with it. The advantage of the proposed …

Use Of The Proof-Of-Stake Algorithm For Distributed Consensus In Blockchain Protocol For Cryptocurrency, Spencer J. Hosack

Honors Scholar Theses

Recent attention to Bitcoin and other cryptocurrencies has opened investors and the public to the realm of digital currency. Greater exposure around the world has led to a frenzy of entry into the market and a test into the long-term feasibility of Bitcoin being able to remain a functioning peer-to-peer (P2P), decentralized currency. Its main structure is supported by the Proof-of-Work (PoW) protocol in which users can elect to participate in determining transaction approval and ensuring an honest blockchain. This system relies on elected users to expend computational power and energy to solve puzzles to prove the accuracy of the …

Process Models Discovery And Traces Classification: A Fuzzy-Bpmn Mining Approach., Kingsley Okoye Dr, Usman Naeem Dr, Syed Islam Dr, Abdel-Rahman H. Tawil Dr, Elyes Lamine Dr

Journal of International Technology and Information Management

The discovery of useful or worthwhile process models must be performed with due regards to the transformation that needs to be achieved. The blend of the data representations (i.e data mining) and process modelling methods, often allied to the field of Process Mining (PM), has proven to be effective in the process analysis of the event logs readily available in many organisations information systems. Moreover, the Process Discovery has been lately seen as the most important and most visible intellectual challenge related to the process mining. The method involves automatic construction of process models from event logs about any domain …

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 …

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 …

Automatically Defined Templates For Improved Prediction Of Non-Stationary, Nonlinear Time Series In Genetic Programming, David Moskowitz

CCAC Theses and Dissertations

Soft methods of artificial intelligence are often used in the prediction of non-deterministic time series that cannot be modeled using standard econometric methods. These series, such as occur in finance, often undergo changes to their underlying data generation process resulting in inaccurate approximations or requiring additional human judgment and input in the process, hindering the potential for automated solutions.

Genetic programming (GP) is a class of nature-inspired algorithms that aims to evolve a population of computer programs to solve a target problem. GP has been applied to time series prediction in finance and other domains. However, most GP-based approaches to …

Feature Selection And Classification Methods For Decision Making: A Comparative Analysis, Osiris Villacampa

CCAC Theses and Dissertations

The use of data mining methods in corporate decision making has been increasing in the past decades. Its popularity can be attributed to better utilizing data mining algorithms, increased performance in computers, and results which can be measured and applied for decision making. The effective use of data mining methods to analyze various types of data has shown great advantages in various application domains. While some data sets need little preparation to be mined, whereas others, in particular high-dimensional data sets, need to be preprocessed in order to be mined due to the complexity and inefficiency in mining high dimensional …

Confidence Weighted Mean Reversion Strategy For Online Portfolio Selection, Bin Li, Steven C. H. Hoi, Peilin Zhao, Vivekanand Gopalkrishnan

Research Collection School Of Computing and Information Systems

Online portfolio selection has been attracting increasing attention from the data mining and machine learning communities. All existing online portfolio selection strategies focus on the first order information of a portfolio vector, though the second order information may also be beneficial to a strategy. Moreover, empirical evidence shows that relative stock prices may follow the mean reversion property, which has not been fully exploited by existing strategies. This article proposes a novel online portfolio selection strategy named Confidence Weighted Mean Reversion (CWMR). Inspired by the mean reversion principle in finance and confidence weighted online learning technique in machine learning, CWMR …

Analyzing The Impact Of Cloud Services Brokers On Cloud Computing Markets, Richard D. Shang, Jianhui Huang, Yinping Yang, Robert J. Kauffman

Research Collection School Of Computing and Information Systems

This research offers a theoretical model of brokered services and provides an analysis of their impact on the cloud computing market with risk preference-based stratification of client segments. The model structures the decision problem that clients face when they choose among spot, reserved and brokered services. Although all the three types of services do not indemnify the cloud services client against other kinds of service outages, due to changes in market demand, service interruptions occur most frequently in the spot market, and are lower when brokered services are offered, and no risk of inter-ruption is involved in reserved services. Based …

On-Line Portfolio Selection With Moving Average Reversion, Bin Li, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

On-line portfolio selection has attracted increasing interests in machine learning and AI communities recently. Empirical evidences show that stock's high and low prices are temporary and stock price relatives are likely to follow the mean reversion phenomenon. While the existing mean reversion strategies are shown to achieve good empirical performance on many real datasets, they often make the single-period mean reversion assumption, which is not always satisfied in some real datasets, leading to poor performance when the assumption does not hold. To overcome the limitation, this article proposes a multiple-period mean reversion, or so-called Moving Average Reversion (MAR), and a …

Framework Developmant For Construction Safety Visialization, Kishor Shrestha

College of Engineering: Graduate Celebration Programs

Throughout the history of the construction industry, many fatalities and injuries have occurred in construction sites. One of the major causes of accidents is unsafe site conditions: basically, this is due to inadequate supervision. To improve upon the traditional supervision approach, this study proposes a 'Framework Development for Construction Safety Visualization' approach. In addition to this, a computer vision Edge Detection Algorithm was developed and tested to convert construction site still images into edges of the objects in the images. The framework development of this study uses computer vision, robot vision, image compression, pattern recognition, internet transmission, network communication, and …

How One Trade Could Change The World: High Frequency Trading And The Flash Crash Of 2010, Sarah Perlman

Honors Projects in Finance

Financial markets are controlled directly by a small population of people, but have direct effects on almost every aspect of the global community. Financial markets are now flooded with computerized algorithms that have drastically changed the face of trading. As with any advances in technology, there are always unforeseen events that create new challenges, and adjustments that need to be made. In our increasingly global and technological world, one wrong click of the mouse in New York could affect the stock markets in London, Tokyo, and Brazil. On May 6th, 2010, such a situation occurred and caused the Dow Jones …

Semantic Inference On Heterogeneous E-Marketplace Activities, Jingzhi Guo, Lida Xu, Zhiguo Gong, Chin-Pang Che, Sohail S. Chaudry

Information Technology & Decision Sciences Faculty Publications

An electronic marketplace (e-marketplace) is a common business information space populated with many entities of different system types. Each of them has its own context of how to process activities. This leads to heterogeneous e-marketplace activities, which are difficult to make interoperable and inferred from one entity to another. This study solves this problem by proposing a concept of separation strategy and implementing it through providing a semantic inference engine with a novel inference algorithm. The solution, called the RuleXPM approach, enables one to semantically infer a next e-marketplace activity across multiple contexts/domains. Experiments show that the cross-context/cross-domain semantic inference …

Image Processing – I: Pointing And Target Selection Of Object Using Color Detection Algorithm Through Dsp Processor Tms320c6711, Waseem Ahmed, M. Irfan, Mr. Muzammil, Mr. Yaseen

International Conference on Information and Communication Technologies

In the field of robotic and image vision technologies, the embedded computing and optimization of the image processing algorithm is quite significant in order to provide the real time processing. In the current era, for making the real time system for such high extensive computation and processing for providing the high speed and real time standalone system, several industrial giants continuously providing the solutions which are in the form of dedicated DSP processors and generalized FPGAs. Beside such high speed processor implementation, the image processing based algorithm plays significant role in machine vision based solution for industries, robotic and field …

Image Processing – I: Automated System For Fingerprint Image Enhancement Using Improved Segmentation And Gabor Wavelets, Amna Saeed, Anam Tariq, Usman Jawaid

International Conference on Information and Communication Technologies

This research revolves around the fingerprint image enhancement. It is used for automated fingerprint identification systems (AFIS) for extracting the best quality fingerprint images. Accurate feature extraction and identification is the basic theme of this enhancement. This paper is on the fingerprint image enhancement using wavelets. Wavelets are famous for their special localization property and orientation flow estimation. The proposed technique is basically comprises of three main steps: segmentation followed by image sharpening and then Gabor wavelet filtering. Segmentation distinguishes between image background and foreground which in turn reduces processing time. Our sharpening stage of enhancement algorithm sharpens the edges …

Open Innovation In Platform Competition, Mei Lin

Research Collection School Of Computing and Information Systems

We examine the competition between a proprietary platform and an open platform,where each platform holds a two-sided market consisted of app developers and users.The open platform cultivates an innovative environment by inviting public efforts todevelop the platform itself and permitting distribution of apps outside of its own appmarket; the proprietary platform restricts apps sales solely within its app market. Weuse a game theoretic model to capture this competitive phenomenon and analyze theimpact of growth of the open source community on the platform competition. We foundthat growth of the open community mitigates the platform rivalry, and balances the developernetwork sizes on …

A Hybrid Scatter Search For The Discrete Time/Resource Trade-Off Problem In Project Scheduling, Mohammad Ranbar, Bert De Reyck, Fereydoon Kianfar

Research Collection Lee Kong Chian School Of Business

We develop a heuristic procedure for solving the discrete time/resource trade-off problem in the field of project scheduling. In this problem, a project contains activities interrelated by finish-start-type precedence constraints with a time lag of zero, which require one or more constrained renewable resources. Each activity has a specified work content and can be performed in different modes, i.e. with different durations and resource requirements, as long as the required work content is met. The objective is to schedule each activity in one of its modes in order to minimize the project makespan. We use a scatter search algorithm to …

A Hybrid Scatter Search/Electromagnetism Meta-Heuristic For Project Scheduling, Dieter Debels, Bert De Reyck, Roel Leus, Mario Vanhoucke

Research Collection Lee Kong Chian School Of Business

In the last few decades, several effective algorithms for solving the resource-constrained project scheduling problem have been proposed. However, the challenging nature of this problem, summarised in its strongly NP-hard status, restricts the effectiveness of exact optimisation to relatively small instances. In this paper, we present a new meta-heuristic for this problem, able to provide near-optimal heuristic solutions for relatively large instances. The procedure combines elements from scatter search, a generic population-based evolutionary search method, and from a recently introduced heuristic method for the optimisation of unconstrained continuous functions based on an analogy with electromagnetism theory. We present computational …

A Model To Evaluate The Effect Of Organizational Adaptation, Holly A. H. Handley, Alexander H. Levis

Engineering Management & Systems Engineering Faculty Publications

When an organization’s output declines due to either internal changes or changes in its external environment, it needs to adapt. In order to evaluate the effectiveness of different adaptation strategies on organizational per- formance, an organizational model composed of individual models of a five stage interacting decision maker was designed using an object oriented design approach and implemented as a Colored Petri net. The concept of entropy is used to calculate the total activity value, a surrogate for decision maker workload, based on the functional partition and the adaptation strategy being implemented. The individual decision maker’s total activity is monitored, …