Theory and Algorithms Commons^™

Trading Cloud Computing Stocks Using Sma, Xianrong Zheng, Lingyu Li

Information Technology & Decision Sciences Faculty Publications

As cloud computing adoption becomes mainstream, the cloud services market offers vast profits. Moreover, serverless computing, the next stage of cloud computing, comes with huge economic potential. To capitalize on this trend, investors are interested in trading cloud stocks. As high-growth technology stocks, investing in cloud stocks is both rewarding and challenging. The research question here is how a trading strategy will perform on cloud stocks. As a result, this paper employs an effective method—Simple Moving Average (SMA)—to trade cloud stocks. To evaluate its performance, we conducted extensive experiments with real market data that spans over 23 years. Results show …

Modeling Coupled Driving Behavior During Lane Change: A Multi-Agent Transformer Reinforcement Learning Approach, Hongyu Guo, Mehdi Keyvan-Ekbatani, Kun Xie

Civil & Environmental Engineering Faculty Publications

In a lane change (LC) scenario, the lane change vehicle interacts with surrounding vehicles. The interactions not only affect their driving behaviors but also influence the traffic flow. This study aims to model the coupled behavior of the lane changer and the follower in the target lane during LC. Large-scale real-world connected vehicle (CV) data from the Safety Pilot Model Deployment (SPMD) program are used to extract LCs and study vehicle interactions. A multi-agent Transformer-based deep deterministic policy gradient (MA-TDDPG) method is proposed to model the coupled behaviors during LC. The multi-agent framework can handle the multiple agents’ behaviors with …

To Thine Own Self Be True? Incentive Problems In Personalized Law, Jordan M. Barry, John William Hatfield, Scott Duke Kominers

William & Mary Law Review

Recent years have seen an explosion of scholarship on “personalized law.” Commentators foresee a world in which regulators armed with big data and machine learning techniques determine the optimal legal rule for every regulated party, then instantaneously disseminate their decisions via smartphones and other “smart” devices. They envision a legal utopia in which every fact pattern is assigned society’s preferred legal treatment in real time.

But regulation is a dynamic process; regulated parties react to law. They change their behavior to pursue their preferred outcomes— which often diverge from society’s—and they will continue to do so under personalized law: They …

Learning From Multi-Class Imbalanced Big Data With Apache Spark, William C. Sleeman Iv

Theses and Dissertations

With data becoming a new form of currency, its analysis has become a top priority in both academia and industry, furthering advancements in high-performance computing and machine learning. However, these large, real-world datasets come with additional complications such as noise and class overlap. Problems are magnified when with multi-class data is presented, especially since many of the popular algorithms were originally designed for binary data. Another challenge arises when the number of examples are not evenly distributed across all classes in a dataset. This often causes classifiers to favor the majority class over the minority classes, leading to undesirable results …

Big Data, Spatial Optimization, And Planning, Kai Cao, Wenwen Li, Richard Church

Research Collection School Of Computing and Information Systems

Spatial optimization represents a set of powerful spatial analysis techniques that can be used to identify optimal solution(s) and even generate a large number of competitive alternatives. The formulation of such problems involves maximizing or minimizing one or more objectives while satisfying a number of constraints. Solution techniques range from exact models solved with such approaches as linear programming and integer programming, or heuristic algorithms, i.e. Tabu Search, Simulated Annealing, and Genetic Algorithms. Spatial optimization techniques have been utilized in numerous planning applications, such as location-allocation modeling/site selection, land use planning, school districting, regionalization, routing, and urban design. These methods …

Disaster Damage Categorization Applying Satellite Images And Machine Learning Algorithm, Farinaz Sabz Ali Pour, Adrian Gheorghe

Engineering Management & Systems Engineering Faculty Publications

Special information has a significant role in disaster management. Land cover mapping can detect short- and long-term changes and monitor the vulnerable habitats. It is an effective evaluation to be included in the disaster management system to protect the conservation areas. The critical visual and statistical information presented to the decision-makers can help in mitigation or adaption before crossing a threshold. This paper aims to contribute in the academic and the practice aspects by offering a potential solution to enhance the disaster data source effectiveness. The key research question that the authors try to answer in this paper is how …

Efficient Reduced Bias Genetic Algorithm For Generic Community Detection Objectives, Aditya Karnam Gururaj Rao

Theses

The problem of community structure identification has been an extensively investigated area for biology, physics, social sciences, and computer science in recent years for studying the properties of networks representing complex relationships. Most traditional methods, such as K-means and hierarchical clustering, are based on the assumption that communities have spherical configurations. Lately, Genetic Algorithms (GA) are being utilized for efficient community detection without imposing sphericity. GAs are machine learning methods which mimic natural selection and scale with the complexity of the network. However, traditional GA approaches employ a representation method that dramatically increases the solution space to be searched by …

Offline And Online Density Estimation For Large High-Dimensional Data, Aref Majdara

Dissertations, Master's Theses and Master's Reports

Density estimation has wide applications in machine learning and data analysis techniques including clustering, classification, multimodality analysis, bump hunting and anomaly detection. In high-dimensional space, sparsity of data in local neighborhood makes many of parametric and nonparametric density estimation methods mostly inefficient.

This work presents development of computationally efficient algorithms for high-dimensional density estimation, based on Bayesian sequential partitioning (BSP). Copula transform is used to separate the estimation of marginal and joint densities, with the purpose of reducing the computational complexity and estimation error. Using this separation, a parallel implementation of the density estimation algorithm on a 4-core CPU is …

Special Issue: Neutrosophic Theories Applied In Engineering, Florentin Smarandache, Jun Ye

Branch Mathematics and Statistics Faculty and Staff Publications

Neutrosophic sets and logic are generalizations of fuzzy and intuitionistic fuzzy sets and logic. Neutrosophic sets and logic are gaining significant attention in solving many real life decision making problems that involve uncertainty, impreciseness, vagueness, incompleteness, inconsistent, and indeterminacy. They have been applied in computational intelligence, multiple criteria decision making, image processing, medical diagnoses, etc. This Special Issue presents original research papers that report on state-of-the-art and recent advancements in neutrosophic sets and logic in soft computing, artificial intelligence, big and small data mining, decision making problems, and practical achievements.

Optimal "Big Data" Aggregation Systems - From Theory To Practical Application, William J. Culhane Iv

Open Access Dissertations

The integration of computers into many facets of our lives has made the collection and storage of staggering amounts of data feasible. However, the data on its own is not so useful to us as the analysis and manipulation which allows manageable descriptive information to be extracted. New tools to extract this information from ever growing repositories of data are required.

Some of these analyses can take the form of a two phase problem which is easily distributed to take advantage of available computing power. The first phase involves computing some descriptive partial result from some subset of the original …

Networked Employment Discrimination, Tamara Kneese

Media Studies

Employers often struggle to assess qualified applicants, particularly in contexts where they receive hundreds of applications for job openings. In an effort to increase efficiency and improve the process, many have begun employing new tools to sift through these applications, looking for signals that a candidate is “the best fit.” Some companies use tools that offer algorithmic assessments of workforce data to identify the variables that lead to stronger employee performance, or to high employee attrition rates, while others turn to third party ranking services to identify the top applicants in a labor pool. Still others eschew automated systems, but …

Data Supply Chains, Tamara Kneese

Media Studies

As data moves between actors and organizations, what emerges is a data supply chain. Unlike manufacturing supply chains, transferred data is often duplicated in the process, challenging the essence of ownership. What does ethical data labor look like? How are the various stakeholders held accountable for being good data guardians? What does clean data transfer look like? What kinds of best practices can business and government put into place? What upstream rights to data providers have over downstream commercialization of their data?

Predicting Human Behavior, Tamara Kneese

Media Studies

Countless highly accurate predictions can be made from trace data, with varying degrees of personal or societal consequence (e.g., search engines predict hospital admission, gaming companies can predict compulsive gambling problems, government agencies predict criminal activity). Predicting human behavior can be both hugely beneficial and deeply problematic depending on the context. What kinds of predictive privacy harms are emerging? And what are the implications for systems of oversight and due process protections? For example, what are the implications for employment, health care and policing when predictive models are involved? How should varied organizations address what they can predict?