Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Publication
- Publication Type
Articles 1 - 5 of 5
Full-Text Articles in Physical Sciences and Mathematics
Bayesian Convolutional Neural Network With Prediction Smoothing And Adversarial Class Thresholds, Noah M. Miller
Bayesian Convolutional Neural Network With Prediction Smoothing And Adversarial Class Thresholds, Noah M. Miller
Theses and Dissertations
Using convolutional neural networks (CNNs) for image classification for each frame in a video is a very common technique. Unfortunately, CNNs are very brittle and have a tendency to be over confident in their predictions. This can lead to what we will refer to as “flickering,” which is when the predictions between frames jump back and forth between classes. In this paper, new methods are proposed to combat these shortcomings. This paper utilizes a Bayesian CNN which allows for a distribution of outputs on each data point instead of just a point estimate. These distributions are then smoothed over multiple …
Crowdtc: Crowd-Powered Learning For Text Classification, Keyu Yang, Yunjun Gao, Lei Liang, Song Bian, Lu Chen, Baihua Zheng
Crowdtc: Crowd-Powered Learning For Text Classification, Keyu Yang, Yunjun Gao, Lei Liang, Song Bian, Lu Chen, Baihua Zheng
Research Collection School Of Computing and Information Systems
Text classification is a fundamental task in content analysis. Nowadays, deep learning has demonstrated promising performance in text classification compared with shallow models. However, almost all the existing models do not take advantage of the wisdom of human beings to help text classification. Human beings are more intelligent and capable than machine learning models in terms of understanding and capturing the implicit semantic information from text. In this article, we try to take guidance from human beings to classify text. We propose Crowd-powered learning for Text Classification (CrowdTC for short). We design and post the questions on a crowdsourcing platform …
A Predictive Model To Predict Cyberattack Using Self-Normalizing Neural Networks, Oluwapelumi Eniodunmo
A Predictive Model To Predict Cyberattack Using Self-Normalizing Neural Networks, Oluwapelumi Eniodunmo
Theses, Dissertations and Capstones
Cyberattack is a never-ending war that has greatly threatened secured information systems. The development of automated and intelligent systems provides more computing power to hackers to steal information, destroy data or system resources, and has raised global security issues. Statistical and Data mining tools have received continuous research and improvements. These tools have been adopted to create sophisticated intrusion detection systems that help information systems mitigate and defend against cyberattacks. However, the advancement in technology and accessibility of information makes more identifiable elements that can be used to gain unauthorized access to systems and resources. Data mining and classification tools …
Deep Learning For Video-Grounded Dialogue Systems, Hung Le
Deep Learning For Video-Grounded Dialogue Systems, Hung Le
Dissertations and Theses Collection (Open Access)
In recent years, we have witnessed significant progress in building systems with artificial intelligence. However, despite advancements in machine learning and deep learning, we are still far from achieving autonomous agents that can perceive multi-dimensional information from the surrounding world and converse with humans in natural language. Towards this goal, this thesis is dedicated to building intelligent systems in the task of video-grounded dialogues. Specifically, in a video-grounded dialogue, a system is required to hold a multi-turn conversation with humans about the content of a video. Given an input video, a dialogue history, and a question about the video, the …
Non-Parametric Stochastic Autoencoder Model For Anomaly Detection, Raphael B. Alampay, Patricia Angela R. Abu
Non-Parametric Stochastic Autoencoder Model For Anomaly Detection, Raphael B. Alampay, Patricia Angela R. Abu
Department of Information Systems & Computer Science Faculty Publications
Anomaly detection is a widely studied field in computer science with applications ranging from intrusion detection, fraud detection, medical diagnosis and quality assurance in manufacturing. The underlying premise is that an anomaly is an observation that does not conform to what is considered to be normal. This study addresses two major problems in the field. First, anomalies are defined in a local context, that is, being able to give quantitative measures as to how anomalies are categorized within its own problem domain and cannot be generalized to other domains. Commonly, anomalies are measured according to statistical probabilities relative to the …