Carlos Alvarez College of Business

Permanent URI for this communityhttps://hdl.handle.net/20.500.12588/250

Nationally ranked and internationally recognized, the UTSA Carlos Alvarez College of Business (COB) offers a comprehensive curriculum that transforms business students into business professionals. The college offers traditional degrees in areas such as accounting, finance and marketing as well as specialized programming in cyber security, data analytics and real estate finance and development. The college is accredited by AACSB International, the Association to Advance Collegiate Schools of Business, placing it among the top 5% of business schools nationwide.

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Robust Optimization for Multiobjective Programming Problems with Imprecise Information
(Elsevier, 2013) Hassanzadeh, Farhad; Nemati, Hamid; Sun, Minghe
A robust optimization approach is proposed for generating nondominated robust solutions for multiobjective linear programming problems with imprecise coefficients in the objective functions and constraints. Robust optimization is used in dealing with impreciseness while an interactive procedure is used in eliciting preference information from the decision maker and in making tradeoffs among the multiple objectives. Robust augmented weighted Tchebycheff programs are formulated from the multiobjective linear programming model using the concept of budget of uncertainty. A linear counterpart of the robust augmented weighted Tchebycheff program is derived. Robust nondominated solutions are generated by solving the linearized counterpart of the robust augmented weighted Tchebycheff programs.
The Role of the Decision-Making Regime on Cooperation in a Workgroup Social Dilemma: An Examination of Cyberloafing
(2015-11-05) Corgnet, Brice; Hernán-González, Roberto; McCarter, Matthew W.
A burgeoning problem facing organizations is the loss of workgroup productivity due to cyberloafing. The current paper examines how changes in the decision-making rights about what workgroup members can do on the job affect cyberloafing and subsequent work productivity. We compare two different types of decision-making regimes: autocratic decision-making and group voting. Using a laboratory experiment to simulate a data-entry organization, we find that, while autocratic decision-making and group voting regimes both curtail cyberloafing (by over 50%), it is only in group voting that there is a substantive improvement (of 38%) in a cyberloafer’s subsequent work performance. Unlike autocratic decision-making, group voting leads to workgroups outperforming the control condition where cyberloafing could not be stopped. Additionally, only in the group voting regime did production levels of cyberloafers and non-loafers converge over time.
Participatory sensing-based semantic and spatial analysis of urban emergency events using mobile social media
(SpringerOpen, 2016-02-09) Xu, Zheng; Zhang, Hui; Sugumaran, Vijayan; Choo, Kim-Kwang Raymond; Mei, Lin; Zhu, Yiwei
With the advances of information communication technologies, it is critical to improve the efficiency and accuracy of emergency management systems through modern data processing techniques. Geographic information system (GIS) models and simulation capabilities are used to exercise response and recovery plans during non-disaster times. They help the decision-makers understand near real-time possibilities during an event. In this paper, a participatory sensing-based model for mining spatial information of urban emergency events is introduced. Firstly, basic definitions of the proposed method are given. Secondly, positive samples are selected to mine the spatial information of urban emergency events. Thirdly, location and GIS information are extracted from positive samples. At last, the real spatial information is determined based on address and GIS information. Moreover, this study explores data mining, statistical analysis, and semantic analysis methods to obtain valuable information on public opinion and requirements based on Chinese microblog data. Typhoon Chan-hom is used as an example. Semantic analysis on microblog data is conducted and high-frequency keywords in different provinces are extracted for different stages of the event. With the geo-tagged and time-tagged data, the collected microblog data can be classified into different categories. Correspondingly, public opinion and requirements can be obtained from the spatial and temporal perspectives to enhance situation awareness and help the government offer more effective assistance.
Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing
(SpringerOpen, 2016-05-10) Osanaiye, Opeyemi; Cai, Haibin; Choo, Kim-Kwang Raymond; Dehghantanha, Ali; Xu, Zheng; Dlodlo, Mqhele
Widespread adoption of cloud computing has increased the attractiveness of such services to cybercriminals. Distributed denial of service (DDoS) attacks targeting the cloud’s bandwidth, services and resources to render the cloud unavailable to both cloud providers, and users are a common form of attacks. In recent times, feature selection has been identified as a pre-processing phase in cloud DDoS attack defence which can potentially increase classification accuracy and reduce computational complexity by identifying important features from the original dataset during supervised learning. In this work, we propose an ensemble-based multi-filter feature selection method that combines the output of four filter methods to achieve an optimum selection. We then perform an extensive experimental evaluation of our proposed method using intrusion detection benchmark dataset, NSL-KDD and decision tree classifier. The findings show that our proposed method can effectively reduce the number of features from 41 to 13 and has a high detection rate and classification accuracy when compared to other classification techniques.
An Improved Apriori Algorithm Based on an Evolution-Communication Tissue-Like P System with Promoters and Inhibitors
(Hindawi, 2017-02-19) Liu, Xiyu; Zhao, Yuzhen; Sun, Minghe
Apriori algorithm, as a typical frequent itemsets mining method, can help researchers and practitioners discover implicit associations from large amounts of data. In this work, a fast Apriori algorithm, called ECTPPI-Apriori, for processing large datasets, is proposed, which is based on an evolution-communication tissue-like P system with promoters and inhibitors. The structure of the ECTPPI-Apriori algorithm is tissue-like and the evolution rules of the algorithm are object rewriting rules. The time complexity of ECTPPI-Apriori is substantially improved from that of the conventional Apriori algorithms. The results give some hints to improve conventional algorithms by using membrane computing models.
A Study of the Triggers of Conflict and Emotional Reactions
(2017-04-20) Caldara, Michael; McBride, Michael T.; McCarter, Matthew W.; Sheremeta, Roman M.
We study three triggers of conflict and explore their resultant emotional reactions in a laboratory experiment. Economists suggest that the primary trigger of conflict is monetary incentives. Social psychologists suggest that conflicts are often triggered by fear. Finally, evolutionary biologists suggest that a third trigger is uncertainty about an opponent’s desire to cause harm. Consistent with the predictions from economics, social psychology, and evolutionary biology, we find that conflict originates from all three triggers. The three triggers differently impact the frequency of conflict, but not the intensity. Also, we find that the frequency and intensity of conflict decrease positive emotions and increase negative emotions and that conflict impacts negative emotions more than positive emotions.
A Novel Certificateless Signature Scheme for Smart Objects in the Internet-of-Things
(2017-05-01) Yeh, Kuo-Hui; Su, Chunhua; Choo, Kim-Kwang Raymond; Chiu, Wayne
Rapid advances in wireless communications and pervasive computing technologies have resulted in increasing interest and popularity of Internet-of-Things (IoT) architecture, ubiquitously providing intelligence and convenience to our daily life. In IoT-based network environments, smart objects are embedded everywhere as ubiquitous things connected in a pervasive manner. Ensuring security for interactions between these smart things is significantly more important, and a topic of ongoing interest. In this paper, we present a certificateless signature scheme for smart objects in IoT-based pervasive computing environments. We evaluate the utility of the proposed scheme in IoT-oriented testbeds, i.e., Arduino Uno and Raspberry PI 2. Experiment results present the practicability of the proposed scheme. Moreover, we revisit the scheme of Wang et al. (2015) and revealed that a malicious super type I adversary can easily forge a legitimate signature to cheat any receiver as he/she wishes in the scheme. The superiority of the proposed certificateless signature scheme over relevant studies is demonstrated in terms of the summarized security and performance comparisons.
GPU-Based Parallel Particle Swarm Optimization Methods for Graph Drawing
(Hindawi, 2017-07-30) Qu, Jianhua; Liu, Xiyu; Sun, Minghe; Qi, Feng
Particle Swarm Optimization (PSO) is a population-based stochastic search technique for solving optimization problems, which has been proven to be effective in a wide range of applications. However, the computational efficiency on large-scale problems is still unsatisfactory. A graph drawing is a pictorial representation of the vertices and edges of a graph. Two PSO heuristic procedures, one serial and the other parallel, are developed for undirected graph drawing. Each particle corresponds to a different layout of the graph. The particle fitness is defined based on the concept of the energy in the force-directed method. The serial PSO procedure is executed on a CPU and the parallel PSO procedure is executed on a GPU. Two PSO procedures have different data structures and strategies. The performance of the proposed methods is evaluated through several different graphs. The experimental results show that the two PSO procedures are both as effective as the force-directed method, and the parallel procedure is more advantageous than the serial procedure for larger graphs.
Comparison of Gene Set Analysis with Various Score Transformations to Test the Significance of Sets of Genes
(Office of the Vice President for Research, 2018) Arreola, Ivan; Han, David
Microarray analysis can help identify changes in gene expression which are characteristic to human diseases. Although genomewide RNA expression analysis has become a common tool in biomedical research, it still remains a major challenge to gain biological insight from such information. Gene Set Analysis (GSA) is an analytical method to understand the gene expression data and extract biological insight by focusing on sets of genes that share biological function, chromosomal regulation or location. Thing systematic mining of different gene-set collections could be useful for discovering potential interesting gene-sets for further investigation. Here, we seek to improve previously proposed GSA methods for detecting statistically significant gene sets via various score transformations.
A New Chaotic Starling Particle Swarm Optimization Algorithm for Clustering Problems
(Hindawi, 2018-08-19) Wang, Lin; Liu, Xiyu; Sun, Minghe; Qu, Jianhua; Wei, Yanmeng
A new method using collective responses of starling birds is developed to enhance the global search performance of standard particle swarm optimization (PSO). The method is named chaotic starling particle swarm optimization (CSPSO). In CSPSO, the inertia weight is adjusted using a nonlinear decreasing approach and the acceleration coefficients are adjusted using a chaotic logistic mapping strategy to avoid prematurity of the search process. A dynamic disturbance term (DDT) is used in velocity updating to enhance convergence of the algorithm. A local search method inspired by the behavior of starling birds utilizing the information of the nearest neighbors is used to determine a new collective position and a new collective velocity for selected particles. Two particle selection methods, Euclidean distance and fitness function, are adopted to ensure the overall convergence of the search process. Experimental results on benchmark function optimization and classic clustering problems verified the effectiveness of this proposed CSPSO algorithm.
Social Media and Customer-Based Brand Equity: An Empirical Investigation in Retail Industry
(2018-09-19) Colicev, Anatoli; Malshe, Ashwin; Pauwels, Koen
As customer-brand engagement progressively shifts to digital domains, understanding social media effects in branding has become a vital issue. Social media effectiveness is especially important for the US retail sector due to intense competition among retailers for consumer attention and engagement on digital channels. Yet, the research on the effectiveness of social media in the retail industry remains sparse. Thus, the purpose of this paper is to investigate how social media affects US retailers’ customer-based brand equity (CBBE) which is an important indicator of brand success. Using a dataset of 15,717 retailer-day observations, the authors empirically test the dynamics between owned and earned social media and CBBE using panel vector autoregression (PVAR). The authors find strong impacts of owned and earned social media on CBBE across the board. However, they find that owned social media harms CBBE of retailers dealing in hedonic and high involvement products. Whereas owned social media helps general retailers in building CBBE, it reduces CBBE of specialty retailers.
Secure and privacy-preserving 3D vehicle positioning schemes for vehicular ad hoc network
(SpringerOpen, 2018-11-29) Pei, Qianwen; Kang, Burong; Zhang, Lei; Choo, Kim-Kwang Raymond
Industrial wireless networks (IWNs) have applications in areas such as critical infrastructure sectors and manufacturing industries such as car manufacturing. In car manufacturing, IWNs can facilitate manufacturers to improve the design of the vehicles by collecting vehicular status and other related data (such an IWN is also known as vehicular ad hoc networks—VANETs). Vehicle positioning is a key functionality in VANETs. Most existing vehicle positioning systems are capable of providing accurate 2D positioning, but the demand for accurate 3D positioning has increased sharply in recent times (e.g., due to the building of more elevated roads). There are, however, security and privacy concerns relating to 3D positioning systems in VANET. In this paper, we propose two secure and privacy-preserving 3D positioning schemes based on vehicle-to-roadside (V2R) and vehicle-to-vehicle (V2V) communications, respectively. Our schemes are based on the round trip time ranging technique which is used to achieve 3D position. The security and the privacy of vehicles in our schemes are guaranteed through a newly designed one-pass authenticated key agreement protocol. Using experiments, we show that a vehicle can determine whether it is on or under an elevated road in a short period of time.
Application of the Cox Proportional Hazards Model for the Quantitative Analysis of LC-MS Proteomics Data
(Office of the Vice President for Research, 2019) Arreola, Ivan; Han, David
Along with quantitative, analytical genomics, proteomics continues to be a growing field for determining the gene and cellular functions at the protein level. As the liquid chromatography mass spectrometryphy (LC-MS) experiments produce protein peak intensities data, statistical and computational techniques are required to conduct quantitative analytical proteomics. The LC-MS proteomics data often have large quantities of missing peak intensities due to censoring of the low-abundance spectral features. Because of this, the observed peak intensities from the LC-MS method are all positive, skewed, and often left-censored. The classical survival analysis methods are ideal to detect differentially expressed proteins among different groups. These methods include the non-parametric rank sum (RS) tests such as the Kolmogorov-Smirnov (KS) and Wilcoxon-Mann-Whitney (WMW) tests, parametric surivival models such as the accelerated failure time (AFT) model with popular lifetime distributions; log-normal (LN), log-logistic (LL), and Weibull (W) for modeling the peak intensity data. As an alternative approach, here we propose the Cox proportional hazards (PH) method, a popular semi-parametric model for modeling survival data. The proposed regression-based method allows for leniency on the hazard function by alleviating the requirements of distribution-specific hazard functions. With the hopes of gaining more insightful biological information for cellular functions at the protein level, the statistical properties of each method are investigated through a simulation study and an application to the Type I diabetes dataset.
Strategic Analysis and Evaluation of Cheesecake Factory’s Supply Chain: Uncertainties, Challenges, and Remedies
(Office of the Vice President for Research, 2019) Farley, Brittany; Kidd, Michele; Morgan, Scot; Leung, Mark T.
In the business world, it is important to maintain a profitable balance between efficiency (cost) and responsiveness (to changes in the market, customer demand, etc.) We took the fundamentals of supply chain theory and used them to analyze the real-world case of The Cheesecake Factory’s retail cheesecake supply chain. After an examination of the background of its supply chain structure, The Cheesecake Factory’s supply and demand uncertainties were first identified and assessed. We reviewed how supply uncertainties are influenced by disruptions to material flow on the supplier side as well as how implied demand uncertainties are influenced by changes in customers’ behavior and preferences. It follows that these different forms of uncertainties led to many supply chain challenges faced by The Cheesecake Factory, and we made remedial recommendations to address those challenges, including adding and continuously improving the flow of information with advances in technology and partnering with ecofriendly farms. Finally, we reviewed the ability and thus sustainability of The Cheesecake Factory to maintain a strategic balance between cost and responsiveness with their high-end cheesecake products given the ongoing challenges. Understanding supply chain variables is key to remaining profitable in business. The Cheesecake Factory’s cheesecake supply chain displays similar operational and consumption characteristics experienced by many other counterpart food processing supply chains. Our strategic analysis and evaluation can offer valuable insight to manage these supply chains and to improve their profitability.
Comparison of Regression Methods to Identify Differential Expression in RNA-Sequencing Count Data from the Serial Analysis of Gene Expression
(Office of the Vice President for Research, 2019) Arreola, Ivan; Han, David
Comparative RNA-sequencing analysis for the Serial Analysis of Gene Expression (SAGE) can help identify changes in gene expression which are characteristic to human diseases. Since the RNA-sequencing experiment measures gene expressions in the form of counts, usually with a large degree of skewness, the analysis methods based on continuous probability distributions are generally inappropriate for modeling this type of data. Currently, the parametric regression techniques for solving this problem are based on the well-known discrete probability distributions such as Poisson and negative binomial. In order to overcome this modeling challenge with higher flexibilities to account for a wide range of dispersion levels, here we introduce an alternative Generalized Linear Model (GLM) based on the Conway-Maxwell-Poisson distribution, also known as COM-Poisson or CMP distribution. The CMP regression model generalizes the standard Poisson and negative binomial regressions, and it is suitable for fitting count data with varying degrees of over- and under-dispersions. Using simulated and real SAGE datasets, the performance of the proposed method is assessed in comparison to the Poisson- and negative binomial-based regression models.
Predicting the Expected Waiting Time of Popular Attractions in Walt Disney World
(Office of the Vice President for Research, 2019) Mendoza, Dayanira; Wu, Wenbo; Leung, Mark T.
Waiting lines are inevitable consequence of imbalance in service operations at modern theme parks. Because of that, parks have introduced different approaches to reduce standard waiting time; some of which are at no extra cost to guests whereas some others require a price premium. These approaches usually feature a variety of schemes by which guests can bypass the standard waiting line or enter an express lane featuring a minimal wait. Our current study primarily develops statistical learning models to analyze the empirical data gathered from “touringplans.com,” which encompasses some of Walt Disney World’s (WDW) popular attractions located in Orlando, Florida. Results from data analysis and visualization indicate that each of the four parks had similar patterns throughout the years of 2012 through 2018. The study also examines the time-temporal effect and found out which rides having more popularity is dependent upon the season (period) in the year. Empirical analytics are then conducted on each of the four parks using regression modeling (statistical learning) to predict the waiting times for a particular ride during a specific season. Overall, a sample of 13 rides (attractions) over 17 seasons are used to model the waiting times at each theme park, yielding a total of 13x17x4 = 884 possible combinations.
A Density Peak Clustering Algorithm Based on the K-Nearest Shannon Entropy and Tissue-Like P System
(Hindawi, 2019-07-31) Jiang, Zhenni; Liu, Xiyu; Sun, Minghe
This study proposes a novel method to calculate the density of the data points based on K-nearest neighbors and Shannon entropy. A variant of tissue-like P systems with active membranes is introduced to realize the clustering process. The new variant of tissue-like P systems can improve the efficiency of the algorithm and reduce the computation complexity. Finally, experimental results on synthetic and real-world datasets show that the new method is more effective than the other state-of-the-art clustering methods.
Emerging Technologies for Future Sensor Networks—Selected Papers from ICGHIT 2019
(2019-09-06) Kim, Byung-Seo; Kim, Sung Won; Zhang, Chi; Guo, Yuanxiong; Umer, Tariq
The International Conference on Green and Human Information Technology (ICGHIT) is an international conference focusing on green and information technologies oriented toward humanity [...]
Optimal Coordination Strategy for International Production Planning and Pollution Abating under Cap-and-Trade Regulations
(2019-09-19) Xin, Baogui; Peng, Wei; Sun, Minghe
Because both pollution emissions and production policies often are international in scope, it is necessary to find optimal coordination strategies for international production planning and pollution abating. Differential game models are developed for multiple neighboring countries to reach optimal decisions on their production planning and pollution abating under cap-and-trade regulations. Non-cooperative and cooperative differential games are presented to depict the optimal tradeoffs between production planning and pollution abating. Hamilton-Jacobi-Bellman (HJB) equations are then employed to analyze the asymmetric and symmetric feedback solutions. Numerical simulations are used to illustrate the results. Five different dividends are also discussed. With the proposed strategies, more improvement will be directed toward production supplies and environmental issues than ever before.
Identification of Tomato Disease Types and Detection of Infected Areas Based on Deep Convolutional Neural Networks and Object Detection Techniques
(Hindawi, 2019-12-16) Wang, Qimei; Qi, Feng; Sun, Minghe; Qu, Jianhua; Xue, Jie
This study develops tomato disease detection methods based on deep convolutional neural networks and object detection models. Two different models, Faster R-CNN and Mask R-CNN, are used in these methods, where Faster R-CNN is used to identify the types of tomato diseases and Mask R-CNN is used to detect and segment the locations and shapes of the infected areas. To select the model that best fits the tomato disease detection task, four different deep convolutional neural networks are combined with the two object detection models. Data are collected from the Internet and the dataset is divided into a training set, a validation set, and a test set used in the experiments. The experimental results show that the proposed models can accurately and quickly identify the eleven tomato disease types and segment the locations and shapes of the infected areas.

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