Research and Publications - Department of Computer Science
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Item Primitive Laurent Completion and Endpoint Defects in a Split–Machin Hermite–Padé System(The University of Alabama, 2026-07) Yu, RunlongHermite–Padé auxiliary functions in Diophantine approximation require high-order vanishing to be realized on an integral source compatible with endpoint specialization. We study this problem for the Split–Machin system generated by 1, e^z, and a two-term arctangent combination equal to π at z = 1. The direct recurrence has an exact linearly growing moment defect. Negative shifts complete the formal moment matrix, but leave the polynomial source and, after Taylor regularization, carry a primitive rank-one endpoint defect. We give two realizations. In the power-series category, canonical balanced generators preserve the origin jet, endpoint equalizer, and fixed Taylor window; their rank and forcing reduce to an explicit residual matrix. In the Laurent category, we construct a primitive saturated source on which the formal completion becomes an actual integral Taylor map through a mark-preserving unimodular comparison. Thus the homogeneous map has full row rank, the forcing kernel is primitive, and the least positive endpoint mark is computable from determinantal divisors. The new Laurent forcing plane carries a nonzero raw endpoint exterior.Item Computer-Assisted Diagonal Hermite–Padé Normality via 239-Adic Discrete Convexity(The University of Alabama, 2026-07) Yu, RunlongWe prove a computer-assisted normality theorem for diagonal type-I Hermite--Pad\'e approximation to the mixed exponential--arctangent system $1,e^z,G(z)$, where $G(z)=16\arctan(z/5)-4\arctan(z/239)$. For every $0\le d\le 1{,}087{,}602{,}879$, the maximal order of vanishing at the origin is the dimension-counting value $3d+2$. The system tests normality beyond classical perfect-system settings: it mixes exponential and logarithmic behavior and has no immediate positivity or standard orthogonality argument. We convert the determinant expansion into a three-scale $239$-adic energy, decompose it into two laminar $M$-convex functions, and reduce unique minimization to an exchange-graph certificate. Uniform localization to at most $62$ vertices and exact periodicity turn the infinite family into a finite exact computation, independently checked over $36{,}041{,}089{,}238$ edge inequalities. The proof separates the analytic and infinite-to-finite reductions from machine verification and illustrates how arithmetic structure, discrete convexity, and auditable certificates can resolve determinant nonvanishing in mixed special-function systems.Item An Inverse Apéry Audit for e + π: Positivity-Smallness Separation(The University of Alabama, 2026-07) Yu, RunlongWe study a finite inverse Apéry program for the open irrationality problem of e + π. The construction starts from an exact integral identity that converts each integer polynomial R in Z[x] into an integer linear form R(1)(e + π) − R(0). We audit whether natural finite search classes can simultaneously provide the main ingredients of an Apéry certificate: integrality, smallness, nonzero certification, and an infinite arithmetic structure. Residual-based lattice searches produce small non-continued-fraction linear forms and small kernels, but only sparse sign dominance. A structured Hermite–Padé restart shows the opposite behavior: sampled one-sign kernels become abundant, while endpoint errors and kernel norms grow enormously. An extended coupled postprocess over 2411 merged candidates still finds no row combining endpoint smallness, kernel smallness, and sign dominance. The tested classes therefore exhibit a positivity–smallness separation: smallness and sign control can be forced separately, but not together within a recognizable Apéry-type structure.Item A Web-Based Geotechnical GIS(Wiley, 2011-10-05) Graettinger, Andrew J.; Ryals, Zachary T.; Smith, Randy K.A web-based Geotechnical Geographic Information System (GeoGIS) was developed and tested for the Alabama Departmentof Transportation. This web-based system stores geotechnical information about transportation projects, such as subsurface data,construction drawings, and design information. Typically, this information is in a report or plan sheet format, but raw geotechnicaldata can also be accommodated in the GeoGIS. The goal of this system is to provide easy access and storage for all geotechnicaland subsurface structural information from across a state. Access through a secure web interface allows for keyword searches andinteractive map selection. The web-based GeoGIS has four geotechnical layers (project, bridge, foundation, and soil boring) thatcan be displayed on a road map, aerial photos, or USGS 7.5 minute quadrangles. For testing purposes the GeoGIS was populatedwith multiple document types, formats, and sizes. In all cases, the system performed above expectations.Item Fundamentals, Algorithms, and Technologies of Occupancy Detection for Smart Buildings Using IoT Sensors(MDPI, 2024-03-26) Chaudhari, Pratiksha; Xiao, Yang; Cheng, Mark Ming-Cheng; Li, TieshanSmart buildings use advanced technologies to automate building functions. One important function is occupancy detection using Internet of Things (IoT) sensors for smart buildings. Occupancy information is useful information to reduce energy consumption by automating building functions such as lighting, heating, ventilation, and air conditioning systems. The information is useful to improve indoor air quality by ensuring that ventilation systems are used only when and where they are needed. Additionally, it is useful to enhance building security by detecting unusual or unexpected occupancy levels and triggering appropriate responses, such as alarms or alerts. Occupancy information is useful for many other applications, such as emergency response, plug load energy management, point-of-interest identification, etc. However, the accuracy of occupancy detection is limited by factors such as real-time occupancy data, sensor placement, privacy concerns, and the presence of pets or objects that can interfere with sensor reading. With the rapid development of IoT sensor technologies and the increasing need for smart building solutions, there is a growing interest in occupancy detection techniques. There is a need to provide a comprehensive survey of these technologies. Although there are some exciting survey papers, they all have limited scopes with different focuses. Therefore, this paper provides a comprehensive overview of the current state-of-the-art occupancy detection methods (including both traditional algorithms and machine learning algorithms) and devices with their advantages and limitations. It surveys and compares fundamental technologies (such as sensors, algorithms, etc.) for smart buildings. Furthermore, the survey provides insights and discussions, which can help researchers, practitioners, and stakeholders develop more effective occupancy detection solutions for smart buildings.Item Genetic Algorithms and Machine Learning(Springer Nature, 1988) Goldberg, David E.; Holland, John H.Item Editorial: deep learning for 5G IoT systems(Springer, 2021) Cheng, Xiaochun; Zhang, Chengqi; Qian, Yi; Aloqaily, Moayad; Xiao, Yang; Middlesex University; University of Technology Sydney; University of Nebraska Lincoln; Qatar University; University of Alabama TuscaloosaItem Food/Non-Food Classification of Real-Life Egocentric Images in Low- and Middle-Income Countries Based on Image Tagging Features(Frontiers, 2021) Chen, Guangzong; Jia, Wenyan; Zhao, Yifan; Mao, Zhi-Hong; Lo, Benny; Anderson, Alex K.; Frost, Gary; Jobarteh, Modou L.; McCrory, Megan A.; Sazonov, Edward; Steiner-Asiedu, Matilda; Ansong, Richard S.; Baranowski, Thomas; Burke, Lora; Sun, Mingui; University of Pittsburgh; Imperial College London; University of Georgia; Boston University; University of Alabama Tuscaloosa; University of Ghana; United States Department of Agriculture (USDA); Baylor College of MedicineMalnutrition, including both undernutrition and obesity, is a significant problemin low- and middle-income countries (LMICs). In order to study malnutrition and develop effective intervention strategies, it is crucial to evaluate nutritional status in LMICs at the individual, household, and community levels. In a multinational research project supported by the Bill & Melinda Gates Foundation, we have been using a wearable technology to conduct objective dietary assessment in sub-Saharan Africa. Our assessment includes multiple diet-related activities in urban and rural families, including food sources (e.g., shopping, harvesting, and gathering), preservation/storage, preparation, cooking, and consumption (e.g., portion size and nutrition analysis). Our wearable device ("eButton" worn on the chest) acquires real-life images automatically during wake hours at preset time intervals. The recorded images, in amounts of tens of thousands per day, are post-processed to obtain the information of interest. Although we expect future Artificial Intelligence (AI) technology to extract the information automatically, at present we utilize AI to separate the acquired images into two binary classes: images with (Class 1) and without (Class 0) edible items. As a result, researchers need only to study Class-1 images, reducing their workload significantly. In this paper, we present a composite machine learning method to perform this classification, meeting the specific challenges of high complexity and diversity in the real-world LMIC data. Our method consists of a deep neural network (DNN) and a shallow learning network (SLN) connected by a novel probabilistic network interface layer. After presenting the details of our method, an image dataset acquired from Ghana is utilized to train and evaluate the machine learning system. Our comparative experiment indicates that the new composite method performs better than the conventional deep learning method assessed by integrated measures of sensitivity, specificity, and burden index, as indicated by the Receiver Operating Characteristic (ROC) curve.Item SoSyM reflections: the 2020 "State of the Journal" report(Springer, 2021) Ergin, Huseyin; Gray, Jeff; Rumpe, Bernhard; Schindler, Martin; Ball State University; University of Alabama Tuscaloosa; RWTH Aachen UniversityItem SoSyM reflections: the 2021 "state of the journal" report(Springer, 2022) Ergin, Huseyin; Gray, Jeff; Rumpe, Bernhard; Schindler, Martin; Ball State University; University of Alabama Tuscaloosa; RWTH Aachen UniversityItem Weak Galerkin methods for second order elliptic interface problems(Elsevier, 2013) Mu, Lin; Wang, Junping; Wei, Guowei; Ye, Xiu; Zhao, Shan; Michigan State University; National Science Foundation (NSF); NSF - Directorate for Mathematical & Physical Sciences (MPS); NSF - Division of Mathematical Sciences (DMS); University of Arkansas Little Rock; University of Arkansas Fayetteville; University of Alabama TuscaloosaWeak Galerkin methods refer to general finite element methods for partial differential equations (PDEs) in which differential operators are approximated by their weak forms as distributions. Such weak forms give rise to desirable flexibilities in enforcing boundary and interface conditions. A weak Galerkin finite element method (WG-FEM) is developed in this paper for solving elliptic PDEs with discontinuous coefficients and interfaces. Theoretically, it is proved that high order numerical schemes can be designed by using the WG-FEM with polynomials of high order on each element. Extensive numerical experiments have been carried out to validate the WG-FEM for solving second order elliptic interface problems. High order of convergence is numerically confirmed in both L-2 and L-infinity norms for the piecewise linear WG-FEM. Special attention is paid to solve many interface problems, in which the solution possesses a certain singularity due to the nonsmoothness of the interface. A challenge in research is to design nearly second order numerical methods that work well for problems with low regularity in the solution. The best known numerical scheme in the literature is of order O(h) to O(h(1.5)) for the solution itself in L-infinity norm. It is demonstrated that the WG-FEM of the lowest order, i.e., the piecewise constant WG-FEM, is capable of delivering numerical approximations that are of order O(h(1.75)) to O(h(2)) in the L-infinity norm for C-1 or Lipschitz continuous interfaces associated with a C-1 or H-2 continuous solution. (C) 2013 Elsevier Inc. All rights reserved.Item A survey of the state of the practice for research software in the United States(PeerJ, 2022) Carver, Jeffrey C.; Weber, Nic; Ram, Karthik; Gesing, Sandra; Katz, Daniel S.; University of Alabama Tuscaloosa; University of Washington; University of Washington Seattle; University of California Berkeley; University of Illinois Urbana-ChampaignResearch software is a critical component of contemporary scholarship. Yet, most research software is developed and managed in ways that are at odds with its long-term sustainability. This paper presents findings from a survey of 1,149 researchers, primarily from the United States, about sustainability challenges they face in developing and using research software. Some of our key findings include a repeated need for more opportunities and time for developers of research software to receive training. These training needs cross the software lifecycle and various types of tools. We also identified the recurring need for better models of funding research software and for providing credit to those who develop the software so they can advance in their careers. The results of this survey will help inform future infrastructure and service support for software developers and users, as well as national research policy aimed at increasing the sustainability of research software.Item Electronic medical records and physician stress in primary care: results from the MEMO Study(Oxford University Press, 2014) Babbott, Stewart; Manwell, Linda Baier; Brown, Roger; Montague, Enid; Williams, Eric; Schwartz, Mark; Hess, Erik; Linzer, Mark; University of Kansas; University of Kansas Medical Center; University of Wisconsin Madison; Northwestern University; University of Alabama Tuscaloosa; New York University; Mayo Clinic; Hennepin County Medical CenterBackground Little has been written about physician stress that may be associated with electronic medical records (EMR). Objective We assessed relationships between the number of EMR functions, primary care work conditions, and physician satisfaction, stress and burnout. Design and participants 379 primary care physicians and 92 managers at 92 clinics from New York City and the upper Midwest participating in the 2001-5 Minimizing Error, Maximizing Outcome (MEMO) Study. A latent class analysis identified clusters of physicians within clinics with low, medium and high EMR functions. Main measures We assessed physician-reported stress, burnout, satisfaction, and intent to leave the practice, and predictors including time pressure during visits. We used a two-level regression model to estimate the mean response for each physician cluster to each outcome, adjusting for physician age, sex, specialty, work hours and years using the EMR. Effect sizes (ES) of these relationships were considered small (0.14), moderate (0.39), and large (0.61). Key results Compared to the low EMR cluster, physicians in the moderate EMR cluster reported more stress (ES 0.35, p=0.03) and lower satisfaction (ES -0.45, p=0.006). Physicians in the high EMR cluster indicated lower satisfaction than low EMR cluster physicians (ES -0.39, p=0.01). Time pressure was associated with significantly more burnout, dissatisfaction and intent to leave only within the high EMR cluster. Conclusions Stress may rise for physicians with a moderate number of EMR functions. Time pressure was associated with poor physician outcomes mainly in the high EMR cluster. Work redesign may address these stressors.Item How to define modeling languages?(Springer, 2023) Combemale, Benoit; Gray, Jeff; Rumpe, Bernhard; Universite de Rennes; University of Alabama Tuscaloosa; RWTH Aachen UniversityItem Automatic Ingestion Monitor Version 2 - A Novel Wearable Device for Automatic Food Intake Detection and Passive Capture of Food Images(IEEE, 2021) Doulah, Abul; Ghosh, Tonmoy; Hossain, Delwar; Imtiaz, Masudul H.; Sazonov, Edward; University of Alabama TuscaloosaUse of food image capture and/or wearable sensors for dietary assessment has grown in popularity. Active - methods rely on the user to take an image of each eating episode. "Passive" methods use wearable cameras that continuously capture images. Most of "passively" captured images are not related to food consumption and may present privacy concerns. In this paper, we propose a novel wearable sensor (Automatic Ingestion Monitor. AIM-2) designed to capture images only during automatically detected eating episodes. The capture method was validated on a dataset collected from 30 volunteers in the community wearing the AIM-2 for 24h in pseudo-free-living and 24h in a free-living environment. The AIM-2 was able to detect food intake over 10-second epochs with a (mean and standard deviation) Fl-score of 81.8 +/- 10.1%. The accuracy of eating episode detection was 82.7%. Out of a total of 180,570 images captured, 8,929 (4.9%) images belonged to detected eating episodes. Privacy concerns were assessed by a questionnaire on a scale 1-7. Continuous capture had concern value of 5.0 +/- 1.6 (concerned) while image capture only during food intake had concern value of 1.9 +/- 1.7 (not concerned). Results suggest that AIM-2 can provide accurate detection of food intake, reduce the number of images for analysis and alleviate the privacy concerns of the users.Item Automatic Count of Bites and Chews From Videos of Eating Episodes(IEEE, 2020) Hossain, Delwar; Ghosh, Tonmoy; Sazonov, Edward; University of Alabama TuscaloosaMethods for measuring of eating behavior (known as meal microstructure) often rely on manual annotation of bites, chews, and swallows on meal videos or wearable sensor signals. The manual annotation may be time consuming and erroneous, while wearable sensors may not capture every aspect of eating (e.g. chews only). The aim of this study is to develop a method to detect and count bites and chews automatically from meal videos. The method was developed on a dataset of 28 volunteers consuming unrestricted meals in the laboratory under video observation. First, the faces in the video (regions of interest, ROI) were detected using Faster R-CNN. Second, a pre-trained AlexNet was trained on the detected faces to classify images as a bite/no bite image. Third, the affine optical flow was applied in consecutively detected faces to find the rotational movement of the pixels in the ROIs. The number of chews in a meal video was counted by converting the 2-D images to a 1-D optical flow parameter and finding peaks. The developed bite and chew count algorithm was applied to 84 meal videos collected from 28 volunteers. A mean accuracy (+/- STD) of 85.4% (+/- 6.3%) with respect to manual annotation was obtained for the number of bites and 88.9% (+/- 7.4%) for the number of chews. The proposed method for an automatic bite and chew counting shows promising results that can be used as an alternative solution to manual annotation.Item Segmentation and Characterization of Chewing Bouts by Monitoring Temporalis Muscle Using Smart Glasses With Piezoelectric Sensor(IEEE, 2017) Farooq, Muhammad; Sazonov, Edward; University of Alabama TuscaloosaSeveral methods have been proposed for automatic and objective monitoring of food intake, but their performance suffers in the presence of speech and motion artifacts. This paper presents a novel sensor system and algorithms for detection and characterization of chewing bouts from a piezoelectric strain sensor placed on the temporalis muscle. The proposed data acquisition device was incorporated into the temple of eyeglasses. The system was tested by ten participants in two part experiments, one under controlled laboratory conditions and the other in unrestricted free-living. The proposed food intake recognition method first performed an energy-based segmentation to isolate candidate chewing segments (instead of using epochs of fixed duration commonly reported in research literature), with the subsequent classification of the segments by linear support vector machine models. On participant level (combining data from both laboratory and free-living experiments), with ten-fold leave-one-out cross-validation, chewing were recognized with average F-score of 96.28% and the resultant area under the curve was 0.97, which are higher than any of the previously reported results. A multivariate regression model was used to estimate chew counts from segments classified as chewing with an average mean absolute error of 3.83% on participant level. These results suggest that the proposed system is able to identify chewing segments in the presence of speech and motion artifacts, as well as automatically and accurately quantify chewing behavior, both under controlled laboratory conditions and unrestricted free-living.Item Posture and Activity Recognition and Energy Expenditure Estimation in a Wearable Platform(IEEE, 2015) Sazonov, Edward; Hegde, Nagaraj; Browning, Raymond C.; Melanson, Edward L.; Sazonova, Nadezhda A.; University of Alabama Tuscaloosa; Colorado State University; University of Colorado DenverThe use of wearable sensors coupled with the processing power of mobile phones may be an attractive way to provide real-time feedback about physical activity and energy expenditure (EE). Here, we describe the use of a shoe-based wearable sensor system (SmartShoe) with a mobile phone for real-time recognition of various postures/physical activities and the resulting EE. To deal with processing power and memory limitations of the phone, we compare the use of support vector machines (SVM), multinomial logistic discrimination (MLD), and multilayer perceptrons (MLP) for posture and activity classification followed by activity-branched EE estimation. The algorithms were validated using data from 15 subjects who performed up to 15 different activities of daily living during a 4-h stay in a room calorimeter. MLD and MLP demonstrated activity classification accuracy virtually identical to SVM (similar to 95%) while reducing the running time and the memory requirements by a factor of >10(3). Comparison of per-minute EE estimation using activity-branched models resulted in accurate EE prediction (RMSE = 0.78 kcal/min for SVM andMLD activity classification, 0.77 kcal/min for MLP versus RMSE of 0.75 kcal/min for manual annotation). These results suggest that low-power computational algorithms can be successfully used for real-time physical activity monitoring and EE estimation on a wearable platform.Item Selective Content Removal for Egocentric Wearable Camera in Nutritional Studies(IEEE, 2020) Abul Hassan, Mohamed; Sazonov, Edward; University of Alabama TuscaloosaAutomatic Ingestion Monitor v2 (AIM-2) is an egocentric camera and sensor that aids monitoring of individual diet and eating behavior by capturing still images throughout the day and using sensor data to detect eating. The images may be used to recognize foods being eaten, eating environment, and other behaviors and daily activities. At the same time, captured images may carry privacy concerning content such as (1) people in social eating and/or bystanders (i.e., bystander privacy); (2) sensitive documents that may appear on a computer screen in the view of AIM-2 (i.e., context privacy). In this paper, we propose a novel approach based on automatic, image redaction for privacy protection by selective content removal by semantic segmentation using a deep learning neural network. The proposed method reported a bystander privacy removal with precision of 0.87 and recall of 0.94 and reported context privacy removal by precision and recall of 0.97 and 0.98. The results of the study showed that selective content removal using deep learning neural network is a much more desirable approach to address privacy concerns for an egocentric wearable camera for nutritional studies.Item Individuals' privacy concerns and adoption of contact tracing mobile applications in a pandemic: A situational privacy calculus perspective(Oxford University Press, 2021) Hassandoust, Farkhondeh; Akhlaghpour, Saeed; Johnston, Allen C.; Auckland University of Technology; University of Queensland; University of Alabama TuscaloosaObjective: The study sought to develop and empirically validate an integrative situational privacy calculus model for explaining potential users' privacy concerns and intention to install a contact tracing mobile application (CTMA). Materials and Methods: A survey instrument was developed based on the extant literature in 2 research streams of technology adoption and privacy calculus. Survey participants (N = 853) were recruited from all 50 U.S. states. Partial least squares structural equation modeling was used to validate and test the model. Results: Individuals' intention to install a CTMA is influenced by their risk beliefs, perceived individual and societal benefits to public health, privacy concerns, privacy protection initiatives (legal and technical protection), and technology features (anonymity and use of less sensitive data). We found only indirect relationships between trust in public health authorities and intention to install CTMA. Sex, education, media exposure, and past invasion of privacy did not have a significant relationship either, but interestingly, older people were slightly more inclined than younger people to install a CTMA. Discussion: Our survey results confirm the initial concerns about the potentially low adoption rates of CTMA. Our model provides public health agencies with a validated list of factors influencing individuals' privacy concerns and beliefs, enabling them to systematically take actions to address these identified issues, and increase CTMA adoption. Conclusions: Developing CTMAs and increasing their adoption is an ongoing challenge for public health systems and policymakers. This research provides an evidence-based and situation-specific model for a better understanding of this theoretically and pragmatically important phenomenon.