Open Access. Powered by Scholars. Published by Universities.®
- Institution
-
- Old Dominion University (100)
- Singapore Management University (41)
- City University of New York (CUNY) (40)
- Southern Methodist University (34)
- Smith College (33)
-
- Technological University Dublin (28)
- Kennesaw State University (26)
- California Polytechnic State University, San Luis Obispo (25)
- The Texas Medical Center Library (25)
- Chapman University (23)
- University of Nebraska - Lincoln (23)
- Dartmouth College (22)
- Embry-Riddle Aeronautical University (21)
- Central Bank of Nigeria (20)
- CCT College Dublin (19)
- New Jersey Institute of Technology (19)
- Virginia Commonwealth University (19)
- Clemson University (18)
- Purdue University (17)
- West Virginia University (17)
- University of Texas at Arlington (16)
- San Jose State University (15)
- University of Louisville (15)
- Central Washington University (14)
- Air Force Institute of Technology (13)
- University of New Mexico (13)
- California State University, San Bernardino (12)
- Chinese Academy of Sciences (12)
- Georgia Southern University (12)
- Louisiana State University (12)
- Keyword
-
- Machine learning (130)
- Machine Learning (105)
- Deep learning (70)
- Artificial Intelligence (46)
- Deep Learning (45)
-
- Artificial intelligence (37)
- Computer Science (35)
- Classification (25)
- Natural Language Processing (24)
- Natural language processing (24)
- Data Science (23)
- Data science (22)
- Neural networks (20)
- Big data (19)
- Computer vision (18)
- Neural Networks (18)
- Computer science (17)
- Data (17)
- Data mining (17)
- NLP (17)
- AI (16)
- COVID-19 (15)
- Cybersecurity (15)
- Data visualization (14)
- Sentiment analysis (14)
- Twitter (13)
- Algorithms (12)
- Clustering (12)
- Computer Vision (12)
- Data analysis (12)
- Publication Year
- Publication
-
- Theses and Dissertations (41)
- Research Collection School Of Computing and Information Systems (37)
- Statistical and Data Sciences: Faculty Publications (32)
- Dissertations (29)
- Computer Science Faculty Publications (26)
-
- Electronic Theses and Dissertations (23)
- Master's Theses (23)
- SMU Data Science Review (23)
- CBN Journal of Applied Statistics (JAS) (20)
- ICT (19)
- Faculty, Staff and Student Publications (18)
- Articles (17)
- Dissertations, Theses, and Capstone Projects (16)
- Graduate Theses, Dissertations, and Problem Reports (ETD) (16)
- Publications and Research (14)
- All Dissertations (13)
- Electrical & Computer Engineering Faculty Publications (13)
- Bulletin of Chinese Academy of Sciences (Chinese Version) (12)
- Computer and Data Science Faculty Publications (12)
- Electronic Theses, Projects, and Dissertations (12)
- College of Graduate Studies: Theses & Dissertations (11)
- Computer Science Faculty Scholarship (11)
- Conference papers (10)
- LSU New Orleans Theses and Dissertations (10)
- All Faculty Scholarship for the College of the Sciences (9)
- All Graduate Theses, Dissertations, and Other Capstone Projects (9)
- Publications (9)
- Theses (9)
- CCIS Networking / SCIS Networking magazines (8)
- Computational and Data Sciences (PhD) Dissertations (8)
- Publication Type
- File Type
Articles 721 - 750 of 1157
Full-Text Articles in Computer Sciences
Cancel Culture: Who Or What Will Be Next?, Christine Trumper
Cancel Culture: Who Or What Will Be Next?, Christine Trumper
Honors Projects in Data Science
This paper utilizes Data Science and Applied Statistic techniques, to perform an analytical dive into Cancel Culture as it is referenced and used on Twitter. The research focuses on analyzing how Cancel Culture has affected the sentiment of Twitter, specifically how it impacts prominent topics in the media that have occurred between February 2021 to September 2021. The development of a topic and sentiment analysis will be based on 1,302,844 Tweets collected using Twitter’s API. Cancel Culture became popularized on social media in the past few years and there is little concrete information regarding its process and the demographics it …
Identifying Factors That Lead To Injury In The Nfl, Matthew Toner
Identifying Factors That Lead To Injury In The Nfl, Matthew Toner
Honors Projects in Data Science
This study hypothesizes that injury-causing factors can be identified through training machine learning models with NFL injury data. The machine learning process entailed web scraping, pre-processing, cleaning, modeling, and analyzing NFL injury data to identify these factors. The features used to model injuries included the following: games played, games started, weight, height, age, year, years of experience, starting position, and team. The four models used to model NFL injuries were Logistic Regression, Decision Trees, Random Forests, and Gradient Boosted Trees. The model with the best performance was the Gradient Boosted Trees model, with an F1 score of 0.508. In addition, …
Twitter's Role In An Increasingly Polarized Political Climate; A Look Into The 2020 Us Elections, Leanne Kendall
Twitter's Role In An Increasingly Polarized Political Climate; A Look Into The 2020 Us Elections, Leanne Kendall
Honors Projects in Data Science
Amidst politically strained times, one might wonder what has cause such an exaggerated gap between the views of democrats and republicans. For years, research has suggested the US’s voting population is becoming increasingly politically polarized, with one of the causes being social media. This study's purpose is to understand more about the role that social media plays in the polarization of parties in the US. The study is comprised of the analysis of over 3,000,000 tweets from 9/22/2020 through 11/10/2020 that mention or are written by senate and presidential candidates. Natural language processing, network graphing, and sentiment analyses were utilized …
*Interactive Earthquake Visualization With Open Data, Matous Hybl
*Interactive Earthquake Visualization With Open Data, Matous Hybl
Campus Research Month
Because earthquakes claim thousands of lives and billions of dollars yearly, there is a great need to recognize patterns in seismic data. While some tools for analysis exist, most geological software is expensive and open earthquake visualizations are limited. In this project, we provide accessible earthquake visualizations aimed to encourage geologists, and science enthusiasts in general, to explore open data using accessible, yet powerful, tools.
Chattanooga Crime Over Time: An Analysis Of Police Incident Open Data, Logan Bateman
Chattanooga Crime Over Time: An Analysis Of Police Incident Open Data, Logan Bateman
Campus Research Month
The police and citizens of Chattanooga may want to know where the most crime occurs, what time of day is crime or police incidents most likely to occur over time. This information can help them understand the crime hotspots in the area. This research work presents a dashboard built upon open data in attempt to bring understanding and insights to the police and citizens about police incidents from the city of Chattanooga over the past five years.
A Web User Interface Image Processing Tool For Classifying The Extent Of Dementia Across Alzheimer’S, Sathvik Prasad Palyam, Robin Ghosh
A Web User Interface Image Processing Tool For Classifying The Extent Of Dementia Across Alzheimer’S, Sathvik Prasad Palyam, Robin Ghosh
ATU Scholars Symposium
Alzheimer's disease (AD) is the most common form of dementia. This project used four image specifications to classify the dementia stages in each patient applying the CNN algorithm. Employing the CNN-based in silico model, the authors successfully classified and predicted the different AD stages and got around 97.19% accuracy. Later, a web interface tool was developed to educate doctors or researchers to check the patients' dementia level based on the MRI brain images and suggest symptoms that strengthen the predicted level of AI. A user uploads the brain scan, which is sent to the backend server, where the image is …
Intra-Hour Solar Forecasting Using Cloud Dynamics Features Extracted From Ground-Based Infrared Sky Images, Guillermo Terrén-Serrano
Intra-Hour Solar Forecasting Using Cloud Dynamics Features Extracted From Ground-Based Infrared Sky Images, Guillermo Terrén-Serrano
Electrical and Computer Engineering ETDs
Due to the increasing use of photovoltaic systems, power grids are vulnerable to the projection of shadows from moving clouds. An intra-hour solar forecast provides power grids with the capability of automatically controlling the dispatch of energy, reducing the additional cost for a guaranteed, reliable supply of energy (i.e., energy storage). This dissertation introduces a novel sky imager consisting of a long-wave radiometric infrared camera and a visible light camera with a fisheye lens. The imager is mounted on a solar tracker to maintain the Sun in the center of the images throughout the day, reducing the scattering effect produced …
Toward Suicidal Ideation Detection With Lexical Network Features And Machine Learning, Ulya Bayram, William Lee, Daniel Santel, Ali Minai, Peggy Clark, Tracy Glauser, John Pestian
Toward Suicidal Ideation Detection With Lexical Network Features And Machine Learning, Ulya Bayram, William Lee, Daniel Santel, Ali Minai, Peggy Clark, Tracy Glauser, John Pestian
Northeast Journal of Complex Systems (NEJCS)
In this study, we introduce a new network feature for detecting suicidal ideation from clinical texts and conduct various additional experiments to enrich the state of knowledge. We evaluate statistical features with and without stopwords, use lexical networks for feature extraction and classification, and compare the results with standard machine learning methods using a logistic classifier, a neural network, and a deep learning method. We utilize three text collections. The first two contain transcriptions of interviews conducted by experts with suicidal (n=161 patients that experienced severe ideation) and control subjects (n=153). The third collection consists of interviews conducted by experts …
Building Interpretable Methods For Identifying Bridge Maintenance Patterns, Akshay Kale
Building Interpretable Methods For Identifying Bridge Maintenance Patterns, Akshay Kale
Computer Science Graduate Research Workshop
No abstract provided.
A New Application Of The Central Limit Theorem, Kenneth Winters
A New Application Of The Central Limit Theorem, Kenneth Winters
Selected Honors Theses
This paper discusses the Central Limit Theorem (CLT) and its applications. The paper gives an introduction to what the CLT is and how it can be applied to real life. Additionally, the paper gives a conceptual understanding of the theorem through various examples and visuals. The paper discusses the applications of the CLT in fields such as computer science, psychology, and political science. The author then suggests a new mathematical theorem as an application of the CLT and provides a proof of the theorem. The new theorem relates to expected value and probabilities of random variables and provides a link …
Unsupervised Learning With Word Embeddings Captures Quiescent Knowledge From Covid-19 And Materials Science Literature, Tasnim H. Gharaibeh
Unsupervised Learning With Word Embeddings Captures Quiescent Knowledge From Covid-19 And Materials Science Literature, Tasnim H. Gharaibeh
Dissertations
Millions of scientific papers are produced each year and the scientific literature is continuing to grow at a head-spinning speed. Thus, massive scientific knowledge exists in solid text, but all these publications make it difficult, if not impossible, for researchers to keep in up to date with discoveries, even within a narrow scientific area. This massive amount of information also makes it difficult to find implicit and hidden connections, relationships, and dependencies within the information that may guide the direction of future research or lead to valuable new insights. So, there is a need for algorithms or models that can …
Assessing Security Risks With The Internet Of Things, Faith Mosemann
Assessing Security Risks With The Internet Of Things, Faith Mosemann
Senior Honors Theses
For my honors thesis I have decided to study the security risks associated with the Internet of Things (IoT) and possible ways to secure them. I will focus on how corporate, and individuals use IoT devices and the security risks that come with their implementation. In my research, I found out that IoT gadgets tend to go unnoticed as a checkpoint for vulnerability. For example, often personal IoT devices tend to have the default username and password issued from the factory that a hacker could easily find through Google. IoT devices need security just as much as computers or servers …
Assessing Photogrammetry Artificial Intelligence In Monumental Buildings’ Crack Digital Detection, Said Maroun, Mostafa Khalifa, Nabil Mohareb
Assessing Photogrammetry Artificial Intelligence In Monumental Buildings’ Crack Digital Detection, Said Maroun, Mostafa Khalifa, Nabil Mohareb
Architecture and Planning Journal (APJ)
Natural and human-made disasters have significant impacts on monumental buildings, threatening them from being deteriorated. If no rapid consolidations took into consideration traumatic accidents would endanger the existence of precious sites. In this context, Beirut's enormous 4th of August 2020 explosion damaged an estimated 640 historical monuments, many volunteers assess damages for more than a year to prevent the more crucial risk of demolitions. This research aims to assist the collaboration ability among photogrammetry science, Artificial Intelligence Model (AIM) and Architectural Coding to optimize the process for better coverage and scientific approach of data specific to the crack disorders to …
Objective Measure Of Working Memory Capacity Using Eye Movements, James Owens, Gavindya Jayawardena, Yasasi Abeysinghe, Vikas G. Ashok, Sampath Jayarathna
Objective Measure Of Working Memory Capacity Using Eye Movements, James Owens, Gavindya Jayawardena, Yasasi Abeysinghe, Vikas G. Ashok, Sampath Jayarathna
Undergraduate Research Symposium
Human-autonomy teaming (HAT) has become an important area of research due to the autonomous systems being developed for different applications, such as remotely controlled aircraft. Many remotely controlled vehicles will be controlled by automated systems, with a human monitor that may be monitoring multiple vehicles simultaneously. The attention and working memory capacity of operators of remote-controlled vehicles must be maintained at appropriate levels during operation. However, there is currently no direct method of determining working memory capacity, which is important because it is a measure for how memory is being stored for a short term and interacting with long term …
Fusionai, A Dna-Sequence-Based Deep Learning Protocol Reduces The False Positives Of Human Fusion Gene Prediction, Pora Kim, Hua Tan, Jiajia Liu, Himansu Kumar, Xiaobo Zhou
Fusionai, A Dna-Sequence-Based Deep Learning Protocol Reduces The False Positives Of Human Fusion Gene Prediction, Pora Kim, Hua Tan, Jiajia Liu, Himansu Kumar, Xiaobo Zhou
Faculty, Staff and Student Publications
Even though there were many tool developments of fusion gene prediction from NGS data, too many false positives are still an issue. Wise use of the genomic features around the fusion gene breakpoints will be helpful to identify reliable fusion genes efficiently. For this aim, we developed FusionAI, a deep learning pipeline predicting human fusion gene breakpoints from DNA sequence. FusionAI is freely available via https://compbio.uth.edu/FusionGDB2/FusionAI. For complete details on the use and execution of this protocol, please refer to Kim et al. (2021b).
Split Classification Model For Complex Clustered Data, Katherine Gerot
Split Classification Model For Complex Clustered Data, Katherine Gerot
Honors Program: Senior Projects (Public)
Classification in high-dimensional data has generated tremendous interest in a multitude of fields. Data in higher dimensions often tend to reside in non-Euclidean metric space. This prevents Euclidean-based classification methodologies, such as regression, from reliably modeling the data. Many proposed models rely on computationally-complex embedding to convert the data to a more usable format. Others, namely the Support Vector Machine, rely on kernel manipulation to implicitly describe the "feature space" to arrive at a non-linear decision boundary. The proposed methodology in this paper seeks to classify complex data in a relatively computationally-simple and explainable manner.
Bcse: Blockchain-Based Trusted Service Evaluation Model Over Big Data, Fengyin Li, Xinying Yu, Rui Ge, Yanli Wang, Yang Cui, Huiyu Zhou
Bcse: Blockchain-Based Trusted Service Evaluation Model Over Big Data, Fengyin Li, Xinying Yu, Rui Ge, Yanli Wang, Yang Cui, Huiyu Zhou
Big Data Mining and Analytics
The blockchain, with its key characteristics of decentralization, persistence, anonymity, and auditability, has become a solution to overcome the overdependence and lack of trust for a traditional public key infrastructure on third-party institutions. Because of these characteristics, the blockchain is suitable for solving certain open problems in the service-oriented social network, where the unreliability of submitted reviews of service vendors can cause serious security problems. To solve the unreliability problems of submitted reviews, this paper first proposes a blockchain-based identity authentication scheme and a new trusted service evaluation model by introducing the scheme into a service evaluation model. The new …
Big Data With Cloud Computing: Discussions And Challenges, Amanpreet Kaur Sandhu
Big Data With Cloud Computing: Discussions And Challenges, Amanpreet Kaur Sandhu
Big Data Mining and Analytics
With the recent advancements in computer technologies, the amount of data available is increasing day by day. However, excessive amounts of data create great challenges for users. Meanwhile, cloud computing services provide a powerful environment to store large volumes of data. They eliminate various requirements, such as dedicated space and maintenance of expensive computer hardware and software. Handling big data is a time-consuming task that requires large computational clusters to ensure successful data storage and processing. In this work, the definition, classification, and characteristics of big data are discussed, along with various cloud services, such as Microsoft Azure, Google Cloud, …
Exploiting More Associations Between Slots For Multi-Domain Dialog State Tracking, Hui Bai, Yan Yang, Jie Wang
Exploiting More Associations Between Slots For Multi-Domain Dialog State Tracking, Hui Bai, Yan Yang, Jie Wang
Big Data Mining and Analytics
Dialog State Tracking (DST) aims to extract the current state from the conversation and plays an important role in dialog systems. Existing methods usually predict the value of each slot independently and do not consider the correlations among slots, which will exacerbate the data sparsity problem because of the increased number of candidate values. In this paper, we propose a multi-domain DST model that integrates slot-relevant information. In particular, certain connections may exist among slots in different domains, and their corresponding values can be obtained through explicit or implicit reasoning. Therefore, we use the graph adjacency matrix to determine the …
Sampling With Prior Knowledge For High-Dimensional Gravitational Wave Data Analysis, He Wang, Zhoujian Cao, Yue Zhou, Zong-Kuan Guo, Zhixiang Ren
Sampling With Prior Knowledge For High-Dimensional Gravitational Wave Data Analysis, He Wang, Zhoujian Cao, Yue Zhou, Zong-Kuan Guo, Zhixiang Ren
Big Data Mining and Analytics
Extracting knowledge from high-dimensional data has been notoriously difficult, primarily due to the so-called "curse of dimensionality" and the complex joint distributions of these dimensions. This is a particularly profound issue for high-dimensional gravitational wave data analysis where one requires to conduct Bayesian inference and estimate joint posterior distributions. In this study, we incorporate prior physical knowledge by sampling from desired interim distributions to develop the training dataset. Accordingly, the more relevant regions of the high-dimensional feature space are covered by additional data points, such that the model can learn the subtle but important details. We adapt the normalizing flow …
Toward Intelligent Financial Advisors For Identifying Potential Clients: A Multitask Perspective, Qixiang Shao, Runlong Yu, Hongke Zhao, Chunli Liu, Mengyi Zhang, Hongmei Song, Qi Liu
Toward Intelligent Financial Advisors For Identifying Potential Clients: A Multitask Perspective, Qixiang Shao, Runlong Yu, Hongke Zhao, Chunli Liu, Mengyi Zhang, Hongmei Song, Qi Liu
Big Data Mining and Analytics
Intelligent Financial Advisors (IFAs) in online financial applications (apps) have brought new life to personal investment by providing appropriate and high-quality portfolios for users. In real-world scenarios, identifying potential clients is a crucial issue for IFAs, i.e., identifying users who are willing to purchase the portfolios. Thus, extracting useful information from various characteristics of users and further predicting their purchase inclination are urgent. However, two critical problems encountered in real practice make this prediction task challenging, i.e., sample selection bias and data sparsity. In this study, we formalize a potential conversion relationship, i.e., user→activated user→client and decompose this relationship into …
A Comparison Of Computational Approaches For Intron Retention Detection, Jiantao Zheng, Cuixiang Lin, Zhenpeng Wu, Hong-Dong Li
A Comparison Of Computational Approaches For Intron Retention Detection, Jiantao Zheng, Cuixiang Lin, Zhenpeng Wu, Hong-Dong Li
Big Data Mining and Analytics
Intron Retention (IR) is an alternative splicing mode through which introns are retained in mature RNAs rather than being spliced in most cases. IR has been gaining increasing attention in recent years because of its recognized association with gene expression regulation and complex diseases. Continuous efforts have been dedicated to the development of IR detection methods. These methods differ in their metrics to quantify retention propensity, performance to detect IR events, functional enrichment of detected IRs, and computational speed. A systematic experimental comparison would be valuable to the selection and use of existing methods. In this work, we conduct an …
The Mathematics Of Risk: An Introduction To Guaranteed Data De-Identification, Kristi Thompson
The Mathematics Of Risk: An Introduction To Guaranteed Data De-Identification, Kristi Thompson
Western Libraries Presentations
This webinar is devoted to the mathematical and theoretical underpinnings of guaranteed data anonymization. Topics covered include an overview of identifiers and quasi-identifiers, an introduction to k-anonymity, a look at some cases where k-anonymity breaks down, and anonymization hierarchies. The presenter will describe a method to assess a survey dataset for anonymization using standard statistical software and consider the question of "anonymization overkill". Much of the academic material looking at data anonymization is quite abstract and aimed at computer scientists, while material aimed at data curators does not always consider recent developments. This webinar is intended to help bridge the …
Autonomous, Long-Range, Sensor Emplacement Using Unmanned Aircraft Systems, Adam Plowcha, Justin Bradley, Jacob Hoberg, Thomas Ammon, Mark Nail, Brittany Duncan, Carrick Detweiler
Autonomous, Long-Range, Sensor Emplacement Using Unmanned Aircraft Systems, Adam Plowcha, Justin Bradley, Jacob Hoberg, Thomas Ammon, Mark Nail, Brittany Duncan, Carrick Detweiler
School of Computing: Faculty Publications
Automated, in-ground sensor emplacement can significantly improve remote, terrestrial, data collection capabilities. Utilizing a multicopter, unmanned aircraft system (UAS) for this purpose allows sensor insertion with minimal disturbance to the target site or surrounding area. However, developing an emplacement mechanism for a small multicopter, autonomy to manage the target selection and implantation process, as well as long-range deployment are challenging to address. We have developed an autonomous, multicopter UAS that can implant subsurface sensor devices. We enhanced the UAS autopilot with autonomy for target and landing zone selection, as well as ensuring the sensor is implanted properly in the ground. …
Directional Pairwise Class Confusion Bias And Its Mitigation, Sudhashree Sayenju, Ramazan Aygun Phd, Jonathan Boardman, Duleep Prasanna Rathgamage Don, Yifan Zhang Phd, Bill Franks, Sereres Johnston Phd, George Lee, Dan Sullivan, Girish Modgil Phd
Directional Pairwise Class Confusion Bias And Its Mitigation, Sudhashree Sayenju, Ramazan Aygun Phd, Jonathan Boardman, Duleep Prasanna Rathgamage Don, Yifan Zhang Phd, Bill Franks, Sereres Johnston Phd, George Lee, Dan Sullivan, Girish Modgil Phd
Published and Grey Literature from PhD Candidates
Recent advances in Natural Language Processing have led to powerful and sophisticated models like BERT (Bidirectional Encoder Representations from Transformers) that have bias. These models are mostly trained on text corpora that deviate in important ways from the text encountered by a chatbot in a problem-specific context. While a lot of research in the past has focused on measuring and mitigating bias with respect to protected attributes (stereotyping like gender, race, ethnicity, etc.), there is lack of research in model bias with respect to classification labels. We investigate whether a classification model hugely favors one class with respect to another. …
Leveraging Machine Learning For Large Scale Analysis Of Publicly-Available Data For Gnss Interference Events, David K. Stamper
Leveraging Machine Learning For Large Scale Analysis Of Publicly-Available Data For Gnss Interference Events, David K. Stamper
Theses and Dissertations
This research documents architecture and implementation of an enhanced interference detection and classification analysis system, using both a database and storage solution utilizing machine learning algorithms to detect changes in Carrier-to-Noise strength over multiple GNSS sites. The system uses publicly-available government supported receivers to detect interference, and built using FOSS packaged as a programming library through Python. Two algorithms are discussed in terms of enhancing interference detection using both non-machine learning and machine learning approaches. Two algorithms are also discussed which are used for classification of events. In addition, an approach to Large Scale data analytics is demonstrated via a …
Integrating Web Applications Into Popular Survey Platforms For Online Experiments, Benjamin Carter, Alessandro Del Ponte
Integrating Web Applications Into Popular Survey Platforms For Online Experiments, Benjamin Carter, Alessandro Del Ponte
Political Science Faculty Articles and Research
Research using custom-made web applications is burgeoning as scholars increasingly conduct their experiments online. We show how researchers can integrate their web applications into popular survey software such as Qualtrics in five simple steps and provide the full JavaScript code and screenshots. This procedure allows participants to seamlessly switch from Qualtrics to their web applications without leaving the survey platform. This integration has two benefits: (1) it eliminates the risk that participants inadvertently drop out of the survey while switching from the survey software to the web application and vice versa; and (2) it saves researchers the fees charged by …
Learning Latent Causal Dynamics, Weiran Yao, Guangyi Chen, Kun Zhang
Learning Latent Causal Dynamics, Weiran Yao, Guangyi Chen, Kun Zhang
Machine Learning Faculty Publications
One critical challenge of time-series modeling is how to learn and quickly correct the model under unknown distribution shifts. In this work, we propose a principled framework, called LiLY, to first recover time-delayed latent causal variables and identify their relations from measured temporal data under different distribution shifts. The correction step is then formulated as learning the low-dimensional change factors with a few samples from the new environment, leveraging the identified causal structure. Specifically, the framework factorizes unknown distribution shifts into transition distribution changes caused by fixed dynamics and time-varying latent causal relations, and by global changes in observation. We …
Iseeq: Information Seeking Question Generation Using Dynamic Meta-Information Retrieval And Knowledge Graphs, Manas Gaur, Kalpa Gunaratna, Vijay Srinivasan, Hongxia Jin
Iseeq: Information Seeking Question Generation Using Dynamic Meta-Information Retrieval And Knowledge Graphs, Manas Gaur, Kalpa Gunaratna, Vijay Srinivasan, Hongxia Jin
Publications
Conversational Information Seeking (CIS) is a relatively new research area within conversational AI that attempts to seek information from end-users in order to understand and satisfy users’ needs. If realized, such a system has far-reaching benefits in the real world; for example, a CIS system can assist clinicians in pre-screening or triaging patients in healthcare. A key open sub-problem in CIS that remains unaddressed in the literature is generating Information Seeking Questions (ISQs) based on a short initial query from the end user. To address this open problem, we propose Information SEEking Question generator (ISEEQ), a novel approach for generating …
Session 5: Equipment Finance Credit Risk Modeling - A Case Study In Creative Model Development & Nimble Data Engineering, Edward Krueger, Landon Thompson, Josh Moore
Session 5: Equipment Finance Credit Risk Modeling - A Case Study In Creative Model Development & Nimble Data Engineering, Edward Krueger, Landon Thompson, Josh Moore
SDSU Data Science Symposium
This presentation will focus first on providing an overview of Channel and the Risk Analytics team that performed this case study. Given that context, we’ll then dive into our approach for building the modeling development data set, techniques and tools used to develop and implement the model into a production environment, and some of the challenges faced upon launch. Then, the presentation will pivot to the data engineering pipeline. During this portion, we will explore the application process and what happens to the data we collect. This will include how we extract & store the data along with how it …