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Shared Context through Multi-Level Attention Transformers for Text Classification
(2021)
Natural language processing (NLP) has seen recent explosive growth by creating artificial intelligence with human-level intelligence. Understanding the context using an attention mechanism could be further improved by fine-tuning their composition...
A Semantic Approach for Automatic Recovery of Software Architecture
(2014)
Open source projects have been continuously growing in popularity. As a result, a number of open source projects begin to play an important role in current software development. In practice, limited assistance has been ...
Distributed RDF query processing and reasoning for big data / linked data
(2014-08-27)
The Linked Data Movement is aimed at converting unstructured and semi-structured
data on the documents to semantically connected documents called the "web of data." This is
based on Resource Description Framework (RDF) ...
Deep Assertion discovery using word embeddings
(University of Missouri -- Kansas City, 2018)
In recent years, there has been explosive growth in the amount of biomedical data
(e.g., publications, notes from EHRs, clinical trial results), with the majority being
unstructured data. As the volume of data is ...
Distributed Collaborative Framework for Deep Learning in Object Detection
(2020)
computational resources to build a model. There is an increasing demand for a practical approach to constructing object detection models adapted to the local context, limited computing resources, and application logics while supporting real-time inferencing...
A Graph Analytics Framework for Knowledge Discovery
(2016)
In the current data movement, numerous efforts have been made to convert and normalize
a large number of traditionally structured and unstructured data to semi-structured data
(e.g., RDF, OWL). With the increasing number ...
StoryNet: A 5W1H-based knowledge graph to connect stories
(2021)
the information from all around the globe. Even though there have been efforts to consolidate the information on a large scale like Wikipedia, Wiki Data, etc, they are devoid of any real-time happenings. With the recent advances in Natural Language Processing (NLP...
iHear – Lightweight Machine Learning Engine with Context Aware Audio Recognition Model
(University of Missouri–Kansas City, 2016)
With the increasing popularity and affordability of smartphones, there is a high demand to add
machine-learning engines to smartphones. However, Machine Learning with smartphones is typically
not feasible due to the heavy ...
Siamese Network-Based Multi-Modal Deepfake Detection
(2020)
for uni-modal or multi-modal deepfake detection with minimum resources. The proposed solution was designed with a Siamese network-based deepfake model with invariant of constructive loss and triplet loss. Contrastive loss uses the trained network's output...
Feature-based Analysis for Open Source using Big Data Analytics
(2015)
The open source code base has increased enormously and hence understanding the functionality of the projects has become extremely difficult. The existing approaches of feature discovery that aim to identify functionality ...
Class Representative Projection for Text-based Zero-Shot Learning
(2020)
There have been significant advances in supervised machine learning and enormous benefits from deep learning for a range of diverse applications. Despite the success of deep learning, in reality, very few works have shown ...
Dynamic Model Generation and Semantic Search for Open Source Projects using Big Data Analytics
(2015)
logic of the program and helps with analyzing the relationships between various classes. The extracted data is then transformed using Natural language processing (NLP) [15] techniques like lemmatization.
In the second step, the transformed data...
KB4DL: Building a Knowledge Base for Deep Learning
(University of Missouri -- Kansas City, 2019)
Deep Learning (DL) has received considerable attention from the AI community. However, we
suffer from the lack of ability in interpretation and annotation of the outcomes from
extensive and exhausting learning efforts. ...
Topic network: a semantic model for effective learning
(University of Missouri--Kansas City, 2011-06-08)
There has been tremendous interest in sharing and retrieving information through the Web. A
search engine can be used to retrieve relevant web documents. However, the sheer volume of
results returned often requires ...
MDRED: Multi-Modal Multi-Task Distributed Recognition for Event Detection
(University of Missouri -- Kansas City, 2018)
Understanding users’ context is essential in emerging mobile sensing applications, such
as Metal Detector, Glint Finder, Facefirst. Over the last decade, Machine Learning (ML)
techniques have evolved dramatically for ...
Software Analytics for Improving Program Comprehension
(2021)
Program comprehension is an essential part of software development and maintenance. Traditional methods of program comprehension, such as reviewing the codebase and documentation, are still challenging for understanding ...
Dynamic Activity Predictions using Graph-based Neural Networks for Time Series Forecasting
(2023)
Time series forecasting is a vital task in numerous fields, and traditional methods,
machine learning models, and neural graph networks have been employed to improve
prediction accuracy. However, these techniques need ...
Domain Playground: Extending Deep Learning Models to Open Domain Boundaries
(2021)
Deep learning models have demonstrated monumental performance in classification tasks but require extensive data and training procedures to converge. Additionally, the performance is only guaranteed when there is no domain ...
Deep Open Representative Learning for Image and Text Classification
(2020)
An essential goal of artificial intelligence is to support the knowledge discovery process from data to the knowledge that is useful in decision making. The challenges in the knowledge discovery process are typically due ...
A semantic framework for event-driven service composition
(University of Missouri--Kansas City, 2011-09-14)
Service Oriented Architecture (SOA) has become a popular paradigm for designing
distributed systems where loosely coupled services (i.e. computational entities) can be
integrated seamlessly to provide complex composite ...