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- Academic -- UNF -- Computing; MACHINE LEARNING; VISUALIZING; LEXICOGRAPHIC PREFERENCE TREES; DATA MINING; CLASSIFICATION; PREFERENCE LEARNING; Lexicographic preference trees -- Visualization; Preference visualization -- Web-based systems; Preference learning -- Genetic algorithms; Dynamic programming algorithms -- Design – Testing; Novel algorithms -- Design – Testing (1)
- Academic -- UNF -- Master of Science in Computer and Information Sciences; Dissertations (1)
- Anomaly detection (1)
- Cloud computing (1)
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- Computer Vision (1)
- Convolutional Neural Network (1)
- Cyber defense (1)
- Cyber operations (1)
- Flatland (1)
- Framing effects (1)
- Interdependency (1)
- Latency-aware Placement (1)
- Machine Learning (1)
- Metaphors (1)
- Microservices (1)
- Migration (1)
- Network function visualization (1)
- Network traffic flows (1)
- Object detection (1)
- Quality of Service (1)
- Re-instantiation (1)
- Redundancy (1)
- Scheduling (1)
- Service Function Chaining (1)
- Single Shot Multibox Detector (SSD) (1)
- Software defined networking (1)
- Thesis; University of North Florida; UNF; Dissertations (1)
- Traffic sign detection (1)
- Unsupervised learning (1)
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Articles 1 - 6 of 6
Full-Text Articles in Other Computer Engineering
Leveraging Cloud-Based Nfv And Sdn Platform Towards Quality-Driven Next-Generation Mobile Networks, Hassan Hawilo
Leveraging Cloud-Based Nfv And Sdn Platform Towards Quality-Driven Next-Generation Mobile Networks, Hassan Hawilo
Electronic Thesis and Dissertation Repository
Network virtualization has become a key approach for Network Service Providers (NSPs) to mitigate the challenge of the continually increasing demands for network services. Tightly coupled with their software components, legacy network devices are difficult to upgrade or modify to meet the dynamically changing end-user needs. To virtualize their infrastructure and mitigate those challenges, NSPs have started to adopt Software Defined Networking (SDN) and Network Function Virtualization (NFV). To this end, this thesis addresses the challenges faced on the road of transforming the legacy networking infrastructure to a more dynamic and agile virtualized environment to meet the rapidly increasing demand …
Cyber Metaphors And Cyber Goals: Lessons From “Flatland”, Pierre Trepagnier
Cyber Metaphors And Cyber Goals: Lessons From “Flatland”, Pierre Trepagnier
Military Cyber Affairs
Reasoning about complex and abstract ideas is greatly influenced by the choice of metaphors through which they are represented. In this paper we consider the framing effect in military doctrine of considering cyberspace as a domain of action, parallel to the traditional domains of land, sea, air, and space. By means of the well-known Victorian science-fiction novella Flatland, we offer a critique of this dominant cyber metaphor. In Flatland, the problems of lower-dimensional beings comprehending additional dimensions are explored at some length. Inspired by Flatland, our suggested alternate metaphor for cyber is an additional (fourth) dimension. We …
Minos: Unsupervised Netflow-Based Detection Of Infected And Attacked Hosts, And Attack Time In Large Networks, Mousume Bhowmick
Minos: Unsupervised Netflow-Based Detection Of Infected And Attacked Hosts, And Attack Time In Large Networks, Mousume Bhowmick
Boise State University Theses and Dissertations
Monitoring large-scale networks for malicious activities is increasingly challenging: the amount and heterogeneity of traffic hinder the manual definition of IDS signatures and deep packet inspection. In this thesis, we propose MINOS, a novel fully unsupervised approach that generates an anomaly score for each host allowing us to classify with high accuracy each host as either infected (generating malicious activities), attacked (under attack), or clean (without any infection). The generated score of each hour is able to detect the time frame of being attacked for an infected or attacked host without any prior knowledge. MINOS automatically creates a personalized traffic …
Adaptation Of A Deep Learning Algorithm For Traffic Sign Detection, Jose Luis Masache Narvaez
Adaptation Of A Deep Learning Algorithm For Traffic Sign Detection, Jose Luis Masache Narvaez
Electronic Thesis and Dissertation Repository
Traffic signs detection is becoming increasingly important as various approaches for automation using computer vision are becoming widely used in the industry. Typical applications include autonomous driving systems, mapping and cataloging traffic signs by municipalities. Convolutional neural networks (CNNs) have shown state of the art performances in classification tasks, and as a result, object detection algorithms based on CNNs have become popular in computer vision tasks. Two-stage detection algorithms like region proposal methods (R-CNN and Faster R-CNN) have better performance in terms of localization and recognition accuracy. However, these methods require high computational power for training and inference that make …
An Application Of Artificial General Intelligence In Board Games, Nathan Skalka
An Application Of Artificial General Intelligence In Board Games, Nathan Skalka
Computer Science Graduate Research Workshop
No abstract provided.
On Learning And Visualizing Lexicographic Preference Trees, Ahmed S. Moussa
On Learning And Visualizing Lexicographic Preference Trees, Ahmed S. Moussa
UNF Graduate Theses and Dissertations
Preferences are very important in research fields such as decision making, recommendersystemsandmarketing. The focus of this thesis is on preferences over combinatorial domains, which are domains of objects configured with categorical attributes. For example, the domain of cars includes car objects that are constructed withvaluesforattributes, such as ‘make’, ‘year’, ‘model’, ‘color’, ‘body type’ and ‘transmission’.Different values can instantiate an attribute. For instance, values for attribute ‘make’canbeHonda, Toyota, Tesla or BMW, and attribute ‘transmission’ can haveautomaticormanual. To this end,thisthesis studiesproblemsonpreference visualization and learning for lexicographic preference trees, graphical preference models that often are compact over complex domains of objects built of …