Pre-Earthquake Ionospheric Perturbation Identification Using Cses Data Via Transfer Learning,
2021
China Earthquake Administration
Pre-Earthquake Ionospheric Perturbation Identification Using Cses Data Via Transfer Learning, Pan Xiong, Cheng Long, Huiyu Zhou, Roberto Battiston, Angelo De Santis, Dimitar Ouzounov, Xuemin Zhang, Xuhui Shen
Mathematics, Physics, and Computer Science Faculty Articles and Research
During the lithospheric buildup to an earthquake, complex physical changes occur within the earthquake hypocenter. Data pertaining to the changes in the ionosphere may be obtained by satellites, and the analysis of data anomalies can help identify earthquake precursors. In this paper, we present a deep-learning model, SeqNetQuake, that uses data from the first China Seismo-Electromagnetic Satellite (CSES) to identify ionospheric perturbations prior to earthquakes. SeqNetQuake achieves the best performance [F-measure (F1) = 0.6792 and Matthews correlation coefficient (MCC) = 0.427] when directly trained on the CSES dataset with a spatial window centered on the earthquake epicenter with the Dobrovolsky …
Improving Accurate Candidates For Missing Data Using Benefit Performance Of (Ml-Som),
2021
Faculty of Computers and Information Technology, Hadhramout University.
Improving Accurate Candidates For Missing Data Using Benefit Performance Of (Ml-Som), Abeer Abdullah Al-Mohdar, Mohamed Abdullah Bamatraf
Hadhramout University Journal of Natural & Applied Sciences
Missing data is one of the major challenges in extracting and analyzing knowledge from datasets. The performance of training quality was affected by the appearance of missing data in a dataset. For this reason, there is a need for a quick and reliable method to find possible solutions in order to provide an accurate system. Therefore, the previous studies provided robust ability of Self Organizing Map (SOM) algorithm to deal with the missing values [6, 20]. However, it has a drawback such as an error rate(ERR) in the missing values that increase huge dataset. This study is mainly based on …
The Forestecology R Package For Fitting And Assessing Neighborhood Models Of The Effect Of Interspecific Competition On The Growth Of Trees,
2021
Smith College
The Forestecology R Package For Fitting And Assessing Neighborhood Models Of The Effect Of Interspecific Competition On The Growth Of Trees, Albert Y. Kim, David N. Allen, Simon P. Couch
Statistical and Data Sciences: Faculty Publications
Neighborhood competition models are powerful tools to measure the effect of interspecific competition. Statistical methods to ease the application of these models are currently lacking. We present the forestecology package providing methods to (a) specify neighborhood competition models, (b) evaluate the effect of competitor species identity using permutation tests, and (cs) measure model performance using spatial cross-validation. Following Allen and Kim (PLoS One, 15, 2020, e0229930), we implement a Bayesian linear regression neighborhood competition model. We demonstrate the package's functionality using data from the Smithsonian Conservation Biology Institute's large forest dynamics plot, part of the ForestGEO global network of research …
Transfer-Learned Pruned Deep Convolutional Neural Networks For Efficient Plant Classification In Resource-Constrained Environments,
2021
Dakota State University
Transfer-Learned Pruned Deep Convolutional Neural Networks For Efficient Plant Classification In Resource-Constrained Environments, Martinson Ofori
Masters Theses & Doctoral Dissertations
Traditional means of on-farm weed control mostly rely on manual labor. This process is time-consuming, costly, and contributes to major yield losses. Further, the conventional application of chemical weed control can be economically and environmentally inefficient. Site-specific weed management (SSWM) counteracts this by reducing the amount of chemical application with localized spraying of weed species. To solve this using computer vision, precision agriculture researchers have used remote sensing weed maps, but this has been largely ineffective for early season weed control due to problems such as solar reflectance and cloud cover in satellite imagery. With the current advances in artificial …
Comparing The Popularity Of Testing Careers Among Canadian, Indian, Chinese, And Malaysian Students,
2021
University of Western Ontario
Comparing The Popularity Of Testing Careers Among Canadian, Indian, Chinese, And Malaysian Students, Luiz Fernando Capretz, Pradeep Waychal, Jingdong Jia, Shuib Basri
Electrical and Computer Engineering Publications
This study attempts to understand motivators and de-motivators that influence the decisions of software students to take up and sustain software testing careers across four different countries, Canada, India, China, and Malaysia. Towards that end, we have developed a cross-sectional, but simple, survey-based instrument. In this study we investigated how software engineering and computer science students perceive and value what they do and their environmental settings. This study found that very few students are keen to take up software testing careers - why is this happening with such an important task in the software life cycle? The common advantages of …
Facilitating Team-Based Data Science: Lessons Learned From The Dsc-Wav Project,
2021
University of Minnesota
Facilitating Team-Based Data Science: Lessons Learned From The Dsc-Wav Project, Chelsey Legacy, Andrew Zieffler, Benjamin S. Baumer, Valerie Barr, Nicholas J. Horton
Statistical and Data Sciences: Faculty Publications
While coursework provides undergraduate data science students with some relevant analytic skills, many are not given the rich experiences with data and computing they need to be successful in the workplace. Additionally, students often have limited exposure to team-based data science and the principles and tools of collaboration that are encountered outside of school. In this paper, we describe the DSC-WAV program, an NSF-funded data science workforce development project in which teams of undergraduate sophomores and juniors work with a local non-profit organization on a data-focused problem. To help students develop a sense of agency and improve confidence in their …
Human Mobility Monitoring Using Wifi: Analysis, Modeling, And Applications,
2021
University of Massachusetts Amherst
Human Mobility Monitoring Using Wifi: Analysis, Modeling, And Applications, Amee Trivedi
Doctoral Dissertations
Understanding and modeling humans and device mobility has fundamental importance in mobile computing, with implications ranging from network design and location-aware technologies to urban infrastructure planning. Today's users carry a plethora of devices such as smartphones, laptops, tablets, and smartwatches, with each device offering a different set of services resulting in different usage and mobility leading to the research question of understanding and modeling multiple user device trajectories. Additionally, prior research on mobility focuses on outdoor mobility when it is known that users spend 80% of their time indoors resulting in wide gaps in knowledge in the area of indoor …
Internet Of Things Software And Hardware Architectures And Their Impacts On Forensic Investigations: Current Approaches And Challenges,
2021
University of Alabama, Huntsville
Internet Of Things Software And Hardware Architectures And Their Impacts On Forensic Investigations: Current Approaches And Challenges, Abel Alex Boozer, Arun John, Tathagata Mukherjee
Journal of Digital Forensics, Security and Law
The never-before-seen proliferation of interconnected low-power computing devices, patently dubbed the Internet of Things (IoT), is revolutionizing how people, organizations, and malicious actors interact with one another and the Internet. Many of these devices collect data in different forms, be it audio, location data, or user commands. In civil or criminal nature investigations, the data collected can act as evidence for the prosecution or the defense. This data can also be used as a component of cybersecurity efforts. When data is extracted from these devices, investigators are expected to do so using proven methods. Still, unfortunately, given the heterogeneity in …
Infer: An R Package For Tidyverse-Friendly Statistical Inference,
2021
Johns Hopkins University
Infer: An R Package For Tidyverse-Friendly Statistical Inference, Simon P. Couch, Andrew P. Bray, Chester Ismay, Evgeni Chasnovski, B. Baumer, Mine Cetinkaya-Rundel
Statistical and Data Sciences: Faculty Publications
infer implements an expressive grammar to perform statistical inference that adheres to the tidyverse design framework (Wickham et al., 2019). Rather than providing methods for specific statistical tests, this package consolidates the principles that are shared among common hypothesis tests and confidence intervals into a set of four main verbs (functions), supplemented with many utilities to visualize and extract value from their outputs.
The Development Of Teaching Case Studies To Explore Ethical Issues Associated With Computer Programming,
2021
Technological University Dublin
The Development Of Teaching Case Studies To Explore Ethical Issues Associated With Computer Programming, Michael Collins, Damian Gordon, Dympna O'Sullivan
Conference papers
In the past decade software products have become pervasive in many aspects of people’s lives around the world. Unfortunately, the quality of the experience an individual has interacting with that software is dependent on the quality of the software itself, and it is becoming more and more evident that many large software products contain a range of issues and errors, and these issues are not known to the developers of these systems, and they are unaware of the deleterious impacts of those issues on the individuals who use these systems. The authors of this paper are developing a new digital …
An Empirical Study Of Thermal Attacks On Edge Platforms,
2021
Kennesaw State University
An Empirical Study Of Thermal Attacks On Edge Platforms, Tyler Holmes
Symposium of Student Scholars
Cloud-edge systems are vulnerable to thermal attacks as the increased energy consumption may remain undetected, while occurring alongside normal, CPU-intensive applications. The purpose of our research is to study thermal effects on modern edge systems. We also analyze how performance is affected from the increased heat and identify preventative measures. We speculate that due to the technology being a recent innovation, research on cloud-edge devices and thermal attacks is scarce. Other research focuses on server systems rather than edge platforms. In our paper, we use a Raspberry Pi 4 and a CPU-intensive application to represent thermal attacks on cloud-edge systems. …
Energy Saving On Edges: State-Of-The-Art And Future Directions,
2021
Kennesaw State University
Energy Saving On Edges: State-Of-The-Art And Future Directions, Kousalya Banka
Symposium of Student Scholars
Internet of Things (IoT) comprises a set of devices that are interconnected ranging from our daily used objects to advanced networked devices. It is a constantly evolving phenomenon as the number of devices owned by the regular user is increasing at a rapid rate. These devices are used for various reasons such as social networking, monitoring, performing complex operations and with the increase of advanced technologies, they demand more energy to perform such tasks. Cloud computing enables these communications to seamlessly perform complex tasks in a cloud environment but utilizing these resources properly to perform at the best is the …
Monitoring At-Home Care Patients Through A Scalar Polar Plot Visualization Of Motion Sensor Data,
2021
Western University
Monitoring At-Home Care Patients Through A Scalar Polar Plot Visualization Of Motion Sensor Data, Michael Mcgavin
Undergraduate Student Research Internships Conference
In Canada, approximately 18 percent (6.6 million) of the total population are age 65 or older, and 88 percent of people over age 65 want to stay in their residence for as long as possible. This older demographic is a group that is dependent on proactive and preventative healthcare. Using motion sensor data collected from a local company providing home-care services to this demographic, a data visualization was constructed to assist users in observing patient behavior and improving their quality of life while maintaining their independence. However, since the collected data is time-based, it results in a dataset that is …
Enhancing Microbiome Host Disease Prediction With Variational Autoencoders,
2021
Chapman University
Enhancing Microbiome Host Disease Prediction With Variational Autoencoders, Celeste Manughian-Peter
Computational and Data Sciences (MS) Theses
Advancements in genetic sequencing methods for microbiomes in recent decades have permitted the collection of taxonomic and functional profiles of microbial communities, accelerating the discovery of the functional aspects of the microbiome and generating an increased interest among clinicians in applying these techniques with patients. This advancement has coincided with software and hardware improvements in the field of machine learning and deep learning. Combined, these advancements implicate further potential for progress in disease diagnosis and treatment in humans. The ability to classify a human microbiome profile into a disease category, and additionally identify the differentiating factors within the profile between …
Identification Of Chemical Structures And Substructures Via Deep Q-Learning And Supervised Learning Of Ftir Spectra,
2021
Missouri State University
Identification Of Chemical Structures And Substructures Via Deep Q-Learning And Supervised Learning Of Ftir Spectra, Joshua D. Ellis
MSU Graduate Theses
Fourier-transform infrared (FTIR) spectra of organic compounds can be used to compare and identify compounds. A mid-FTIR spectrum gives absorbance values of a compound over the 400-4000 cm-1 range. Spectral matching is the process of comparing the spectral signature of two or more compounds and returning a value for the similarity of the compounds based on how closely their spectra match. This process is commonly used to identify an unknown compound by searching for its spectrum’s closes match in a database of known spectra. A major limitation of this process is that it can only be used to identify …
Automated Parsing Of Flexible Molecular Systems Using Principal Component Analysis And K-Means Clustering Techniques,
2021
Chapman University
Automated Parsing Of Flexible Molecular Systems Using Principal Component Analysis And K-Means Clustering Techniques, Matthew J. Nwerem
Computational and Data Sciences (MS) Theses
Computational investigation of molecular structures and reactions of biological and pharmaceutical interests remains a grand scientific challenge due to the size and conformational flexibility of these systems. The work requires parsing and analyzing thousands of conformations in each molecular state for meaningful chemical information and subjecting the ensemble to costly quantum chemical calculations. The current status quo typically involves a manual process where the investigator must look at each conformation, separating each into structural families. This process is time-intensive and tedious, making this process infeasible in some cases, and limiting the ability of theoreticians to study these systems. However, the …
Towards Understanding The Temporal Accuracy Of Openstreetmap: A Quantitative Experiment,
2021
Florida International University
Towards Understanding The Temporal Accuracy Of Openstreetmap: A Quantitative Experiment, Levente Juhasz
GIS Center
No abstract provided.
Insights And Lessons Learned From The Design, Development And Deployment Of Pervasive Location-Based Mobile Systems “In The Wild”,
2021
Rochester Institute of Technology
Insights And Lessons Learned From The Design, Development And Deployment Of Pervasive Location-Based Mobile Systems “In The Wild”, Konstantinos Papangelis, Alan Chamberlain, Nicolas Lalone, Ting Cao
Presentations and other scholarship
This paper, based on a reflective approach, presents several insights and lessons learned from the design, development, and deployment of a location-based social network and a location-based game. These are analyzed and discussed against the life-cycle of our studies and range from engaging with the participants to dealing with technical issues while on the field. Overall, the insights and lessons learned illustrate that one should be prepared and flexible enough to accommodate any issues as they arise in a professional manner considering not only the results of the study but also the participants and the researchers involved.The aim of this …
Locating Identities In Time: An Examination Of The Impact Of Temporality On Presentations Of The Self Through Location-Based Social Networks,
2021
Rochester Institute of Technology
Locating Identities In Time: An Examination Of The Impact Of Temporality On Presentations Of The Self Through Location-Based Social Networks, Konstantinos Papangelis, Ioanna Lykourentzou, Vassilis-Javed Khan, Alan Chamberlain, Ting Cao, Micahel Saker, Nicolas Lalone
Articles
Studies of identity and location-based social networks (LBSN) have tended to focus on the performative aspects associated with marking one’s location. Yet, these studies often present this practice as being an a priori aspect of locative media. What is missing from this research is a more granular understanding of how this process develops over time. Accordingly, we focus on the first six weeks of 42 users beginning to use an LBSN we designed and named GeoMoments. Through our analysis of our users' activities, we contribute to understanding identity and LBSN in two distinct ways. First, we show how LBSN users …
Analysis Of The Slo Bay Microbiome From A Network Perspective,
2021
California Polytechnic State University, San Luis Obispo
Analysis Of The Slo Bay Microbiome From A Network Perspective, Lien Viet Nguyen
Master's Theses
Microorganisms are key players in the ecosystem functioning. In this thesis, we developed a framework to preprocess raw microbiome data, build a correlation network, and analyze co-occurrence patterns between microbes. We then applied this framework to a marine microbiome dataset. The dataset used in this study comes from a year-long time-series to characterize the microbial communities in our coastal waters off the Cal Poly Pier. In analyzing this dataset, we were able to observe and confirm previously discovered patterns of interactions and generate hypotheses about new patterns. The analysis of co-occurrences between prokaryotic and eukaryotic taxa is relatively novel and …
