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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A Conway–Maxwell–Poisson Type Generalization of Hypergeometric Distribution
(2023-02-02) Roy, Sudip; Tripathi, Ram C.; Balakrishnan, Narayanaswamy
The hypergeometric distribution has gained its importance in practice as it pertains to sampling without replacement from a finite population. It has been used to estimate the population size of rare species in ecology, discrete failure rate in reliability, fraction defective in quality control, and the number of initial faults present in software coding. Recently, Borges et al. considered a COM type generalization of the binomial distribution, called COM–Poisson–Binomial (CMPB) and investigated many of its characteristics and some interesting applications. In the same spirit, we develop here a generalization of the hypergeometric distribution, called the COM–hypergeometric distribution. We discuss many of its characteristics such as the limiting forms, the over- and underdispersion, and the behavior of its failure rate. We write its probability-generating function (pgf) in the form of Kemp’s family of distributions when the newly introduced shape parameter is a positive integer. In this form, closed-form expressions are derived for its mean and variance. Finally, we develop statistical inference procedures for the model parameters and illustrate the results by extensive Monte Carlo simulations.
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.
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 Study on the X¯ and S Control Charts with Unequal Sample Sizes
(2020-05-02) Park, Chanseok; Wang, Min
The control charts based on X¯ and S are widely used to monitor the mean and variability of variables and can help quality engineers identify and investigate causes of the process variation. The usual requirement behind these control charts is that the sample sizes from the process are all equal, whereas this requirement may not be satisfied in practice due to missing observations, cost constraints, etc. To deal with this situation, several conventional methods were proposed. However, some methods based on weighted average approaches and an average sample size often result in degraded performance of the control charts because the adopted estimators are biased towards underestimating the true population parameters. These observations motivate us to investigate the existing methods with rigorous proofs and we provide a guideline to practitioners for the best selection to construct the X¯ and S control charts when the sample sizes are not equal.
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.
Big Data Analytics, Data Science, ML&AI for Connected, Data-driven Precision Agriculture and Smart Farming Systems: Challenges and Future Directions
(Association for Computing Machinery, 2023-05-09) Han, David; Rodriguez, Mia
Big data and data scientific applications in the modern agriculture are rapidly evolving as the data technology advances and more computational power becomes available. The adoption of big data has enabled farmers and producers to optimize their agricultural activities sustainably with cutting-edge technologies, resulting in eco-friendly and efficient farming. Wireless sensor networks and machine learning have had a direct impact on smart and precision agriculture, with deep learning techniques applied to data collected via sensor nodes. Additionally, internet of things, drones, and robotics are being incorporated into farming techniques. Digital data handling has amplified the information wave, and information and communication technology have been used to deliver benefits to both farmers and consumers. This work highlights the technological implications and challenges that arise in data-driven agricultural practices as well as the research problems that need to be solved.
Can Hierarchical Transformers Learn Facial Geometry?
(2023-01-13) Young, Paul; Ebadi, Nima; Das, Arun; Bethany, Mazal; Desai, Kevin; Najafirad, Peyman
Human faces are a core part of our identity and expression, and thus, understanding facial geometry is key to capturing this information. Automated systems that seek to make use of this information must have a way of modeling facial features in a way that makes them accessible. Hierarchical, multi-level architectures have the capability of capturing the different resolutions of representation involved. In this work, we propose using a hierarchical transformer architecture as a means of capturing a robust representation of facial geometry. We further demonstrate the versatility of our approach by using this transformer as a backbone to support three facial representation problems: face anti-spoofing, facial expression representation, and deepfake detection. The combination of effective fine-grained details alongside global attention representations makes this architecture an excellent candidate for these facial representation problems. We conduct numerous experiments first showcasing the ability of our approach to address common issues in facial modeling (pose, occlusions, and background variation) and capture facial symmetry, then demonstrating its effectiveness on three supplemental tasks.
Centralized versus Decentralized Cleanup of River Water Pollution: An Application to the Ganges
(MDPI, 2023-10-07) Batabyal, Amitrajeet A.; Beladi, Hamid
We exploit the public good attributes of Ganges water pollution cleanup and theoretically analyze an aggregate economy of two cities—Kanpur and Varanasi—through which the Ganges flows. Our specific objective is to study whether water pollution cleanup in these two cities ought to be provided in a centralized or in a decentralized manner. We first determine the efficient cleanup amounts that maximize the aggregate surplus from making the Ganges cleaner in the two cities. Second, we compute the optimal amount of water pollution cleanup in the two cities in a decentralized regime in which spending on cleanup is financed by a uniform tax on the city residents. Third, we ascertain the optimal amount of water pollution cleanup in the two cities in a centralized regime subject to equal provision of cleanup and cost sharing. Fourth, we show that if the two cities have the same preference for pollution cleanup, then centralization is preferable to decentralization as long as there is a spillover from pollution cleanup. Finally, we show that if the two cities have dissimilar preferences for pollution cleanup, then centralization is preferable to decentralization as long as the spillover exceeds a certain threshold.
Classical and Bayesian Inference of a Progressive-Stress Model for the Nadarajah–Haghighi Distribution with Type II Progressive Censoring and Different Loss Functions
(2022-05-08) Alotaibi, Refah Mohammed; Alamri, Faten S.; Almetwally, Ehab M.; Wang, Min; Rezk, Hoda
Accelerated life testing (ALT) is a time-saving technology used in a variety of fields to obtain failure time data for test units in a fraction of the time required to test them under normal operating conditions. This study investigated progressive-stress ALT with progressive type II filtering with the lifetime of test units following a Nadarajah–Haghighi (NH) distribution. It is assumed that the scale parameter of the distribution obeys the inverse power law. The maximum likelihood estimates and estimated confidence intervals for the model parameters were obtained first. The Metropolis–Hastings (MH) algorithm was then used to build Bayes estimators for various squared error loss functions. We also computed the highest posterior density (HPD) credible ranges for the model parameters. Monte Carlo simulations were used to compare the outcomes of the various estimation methods proposed. Finally, one data set was analyzed for validation purposes.
Clinical and Quality of Life Benefits for End-Stage Workers' Compensation Chronic Pain Claimants following H-Wave(R) Device Stimulation: A Retrospective Observational Study with Mean 2-Year Follow-Up
(2023-02-01) Trinh, Alan; Williamson, Tyler K.; Han, David; Hazlewood, Jeffrey E.; Norwood, Stephen M.; Gupta, Ashim
Previously promising short-term H-Wave(R) device stimulation (HWDS) outcomes prompted this retrospective cohort study of the longer-term effects on legacy workers' compensation chronic pain claimants. A detailed chart-review of 157 consecutive claimants undergoing a 30-day HWDS trial (single pain management practice) from February 2018 to November 2019 compiled data on pain, restoration of function, quality of life (QoL), and polypharmacy reduction into a summary spreadsheet for an independent statistical analysis. Non-beneficial trials in 64 (40.8%) ended HWDS use, while 19 (12.1%) trial success charts lacked adequate data for assessing critical outcomes. Of the 74 final treatment study group charts, missing data points were removed for a statistical analysis. Pain chronicity was 7.8 years with 21.6 ± 12.2 months mean follow-up. Mean pain reduction was 35%, with 89% reporting functional improvement. Opioid consumption decreased in 48.8% of users and 41.5% completely stopped; polypharmacy decreased in 36.8% and 24.4% stopped. Zero adverse events were reported and those who still worked usually continued working. An overall positive experience occurred in 66.2% (p < 0.0001), while longer chronicity portended the risk of trial or treatment failure. Positive outcomes in reducing pain, opioid/polypharmacy, and anxiety/depression, while improving function/QoL, occurred in these challenging chronic pain injury claimants. Level of evidence: III
The Communication of Justice, Injustice, and Necessary Evils: An Empirical Examination
(SAGE Publications, 2021-09-22) Thornton-Lugo, Meghan A.; Rupp, Deborah E.
The prevailing approach to studying justice in the workplace has focused on recipients and observers of justice. This approach, however, fails to consider the experience of other parties including those who communicate justice. To understand the experience of communicating fairness, we investigated how justice, injustice, and necessary evils differentially affect guilt and stress. In addition, we explored how communicating bad news compares to these experiences. Across two studies, we found evidence showing that guilt and stress were affected by what was being communicated, such that injustice and necessary evils provoked more guilt and stress than justice. These findings highlight how justice broadly affects communicators psychologically and physiologically.
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.
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.
Deep Learning-Based Intrusion Detection for Distributed Denial of Service Attack in Agriculture 4.0
(2021-05-25) Ferrag, Mohamed Amine; Shu, Lei; Djallel, Hamouda; Choo, Kim-Kwang Raymond
Smart Agriculture or Agricultural Internet of things, consists of integrating advanced technologies (e.g., NFV, SDN, 5G/6G, Blockchain, IoT, Fog, Edge, and AI) into existing farm operations to improve the quality and productivity of agricultural products. The convergence of Industry 4.0 and Intelligent Agriculture provides new opportunities for migration from factory agriculture to the future generation, known as Agriculture 4.0. However, since the deployment of thousands of IoT based devices is in an open field, there are many new threats in Agriculture 4.0. Security researchers are involved in this topic to ensure the safety of the system since an adversary can initiate many cyber attacks, such as DDoS attacks to making a service unavailable and then injecting false data to tell us that the agricultural equipment is safe but in reality, it has been theft. In this paper, we propose a deep learning-based intrusion detection system for DDoS attacks based on three models, namely, convolutional neural networks, deep neural networks, and recurrent neural networks. Each model’s performance is studied within two classification types (binary and multiclass) using two new real traffic datasets, namely, CIC-DDoS2019 dataset and TON_IoT dataset, which contain different types of DDoS attacks.
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.
A DNA algorithm for the job shop scheduling problem based on the Adleman-Lipton model
(Public Library of Science (PLOS), 2020-12-02) Tian, Xiang; Liu, Xiyu; Zhang, Hongyan; Sun, Minghe; Zhao, Yuzhen
A DNA (DeoxyriboNucleic Acid) algorithm is proposed to solve the job shop scheduling problem. An encoding scheme for the problem is developed and DNA computing operations are proposed for the algorithm. After an initial solution is constructed, all possible solutions are generated. DNA computing operations are then used to find an optimal schedule. The DNA algorithm is proved to have an O(n2) complexity and the length of the final strand of the optimal schedule is within appropriate range. Experiment with 58 benchmark instances show that the proposed DNA algorithm outperforms other comparative heuristics.
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 [...]
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 Extended Clustering Membrane System Based on Particle Swarm Optimization and Cell-Like P System with Active Membranes
(Hindawi, 2020-01-31) Wang, Lin; Liu, Xiyu; Sun, Minghe; Qu, Jianhua
An extended clustering membrane system using a cell-like P system with active membranes based on particle swarm optimization (PSO), named PSO-CP, is designed, developed, implemented, and tested. The purpose of PSO-CP is to solve clustering problems. In PSO-CP, evolution rules based on the standard PSO mechanism are used to evolve the objects and communication rules are adopted to accelerate convergence and avoid prematurity. Subsystems of membranes are generated and dissolved by the membrane creation and dissolution rules, and a modified PSO mechanism is developed to help the objects escape from local optima. Under the control of the evolution-communication mechanism, the extended membrane system can effectively search for the optimal partitioning and improve the clustering performance with the help of the distributed parallel computing model. This extended clustering membrane system is compared with five existing PSO clustering approaches using ten benchmark clustering problems, and the computational results demonstrate the effectiveness of PSO-CP.
Fuzzy Reasoning Numerical Spiking Neural P Systems for Induction Motor Fault Diagnosis
(2022-09-28) Yin, Xiu; Liu, Xiyu; Sun, Minghe; Dong, Jianping; Zhang, Gexiang
The fuzzy reasoning numerical spiking neural P systems (FRNSN P systems) are proposed by introducing the interval-valued triangular fuzzy numbers into the numerical spiking neural P systems (NSN P systems). The NSN P systems were applied to the SAT problem and the FRNSN P systems were applied to induction motor fault diagnosis. The FRNSN P system can easily model fuzzy production rules for motor faults and perform fuzzy reasoning. To perform the inference process, a FRNSN P reasoning algorithm was designed. During inference, the interval-valued triangular fuzzy numbers were used to characterize the incomplete and uncertain motor fault information. The relative preference relationship was used to estimate the severity of various faults, so as to warn and repair the motors in time when minor faults occur. The results of the case studies showed that the FRNSN P reasoning algorithm can successfully diagnose single and multiple induction motor faults and has certain advantages over other existing methods.

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