Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Keyword
-
- Behavior-Based (1)
- Certificate-based signature (1)
- Command and control (1)
- Computational Diffie–Hellman assumption (1)
- Dipolar (1)
-
- Distributed multi-agent system (1)
- Engineering, Computer Engineering, Computer Sciences, Digital Communications and Networking, Information Security (1)
- Formal Verification (1)
- Forward security (1)
- Hierarchical peer to peer (1)
- Image annotation (1)
- Image retrieval (1)
- Insider threat detection (1)
- Large-scale (1)
- Malicious insider behavior (1)
- Model (1)
- Ontology (1)
- PDPA (1)
- Protein (1)
- RDC (1)
- Random oracle model (1)
- Robotics (1)
- Structure (1)
- Unassigned (1)
- VMI (1)
- Validation (1)
- Virtual machine introspection (1)
Articles 1 - 8 of 8
Full-Text Articles in Physical Sciences and Mathematics
Getting It Right The First Time: Robot Mission Guarantees In The Presence Of Uncertainty, Damian Lyons, Ron Arkin, Paramesh Nirmal, Shu Jiang, Tsung-Ming Liu, Julia Deeb
Getting It Right The First Time: Robot Mission Guarantees In The Presence Of Uncertainty, Damian Lyons, Ron Arkin, Paramesh Nirmal, Shu Jiang, Tsung-Ming Liu, Julia Deeb
Faculty Publications
Abstract—Certain robot missions need to perform predictably in a physical environment that may only be poorly characterized in advance. We have previously developed an approach to establishing performance guarantees for behavior-based controllers in a process-algebra framework. We extend that work here to include random variables, and we show how our prior results can be used to generate a Dynamic Bayesian Network for the coupled system of program and environment model. Verification is reduced to a filtering problem for this network. Finally, we present validation results that demonstrate the effectiveness of the verification of a multiple waypoint robot mission using this …
Protein Structure Validation And Identification From Unassigned Residual Dipolar Coupling Data Using 2d-Pdpa, Arjang Fahim, Rishi Mukhopadhayay, Ryan Yandle, James H. Prestegard, Homayoun Valafar
Protein Structure Validation And Identification From Unassigned Residual Dipolar Coupling Data Using 2d-Pdpa, Arjang Fahim, Rishi Mukhopadhayay, Ryan Yandle, James H. Prestegard, Homayoun Valafar
Faculty Publications
More than 90% of protein structures submitted to the PDB each year are homologous to some previously characterized protein structure. The extensive resources that are required for structural characterization of proteins can be justified for the 10% of the novel structures, but not for the remaining 90%. This report presents the 2D-PDPA method, which utilizes unassigned residual dipolar coupling in order to address the economics of structure determination of routine proteins by reducing the data acquisition and processing time. 2D-PDPA has been demonstrated to successfully identify the correct structure of an array of proteins that range from 46 to 445 …
A Forward-Secure Certificate-Based Signature Scheme, Jiguo Li, Huiyun Teng, Xinyu Huang, Yichen Zhang, Jianying Zhou
A Forward-Secure Certificate-Based Signature Scheme, Jiguo Li, Huiyun Teng, Xinyu Huang, Yichen Zhang, Jianying Zhou
Faculty Publications
Cryptographic computations are often carried out on insecure devices for which the threat of key exposure raises a serious concern. In an effort to address the key exposure problem, the notion of forward security was first presented by Günther in 1990. In a forward-secure scheme, secret keys are updated at regular periods of time; exposure of the secret key corresponding to a given time period does not enable an adversary to ‘break’ the scheme for any prior time period. In this paper, we first introduce forward security into certificate-based cryptography and define the security model of forward-secure certificate-based signatures (CBSs). …
Context-Driven Image Annotation Using Imagenet, George E. Noel, Gilbert L. Peterson
Context-Driven Image Annotation Using Imagenet, George E. Noel, Gilbert L. Peterson
Faculty Publications
Image annotation research has demonstrated success on test data for focused domains. Unfortunately, extending these techniques to the broader topics found in real world data often results in poor performance. This paper proposes a novel approach that leverages WordNet and ImageNet capabilities to annotate images based on local text and image features. Signatures generated from ImageNet images based on WordNet synonymous sets are compared using Earth Mover's Distance against the query image and used to rank order surrounding words by relevancy. The results demonstrate effective image annotation, producing higher accuracy and improved specificity over the ALIPR image annotation system. Abstract …
Application Of Social Network Metrics To A Trust-Aware Collaborative Model For Generating Personalized User Recommendations, Iraklis Varlamis, Magdalini Eirinaki, Malamati Louta
Application Of Social Network Metrics To A Trust-Aware Collaborative Model For Generating Personalized User Recommendations, Iraklis Varlamis, Magdalini Eirinaki, Malamati Louta
Faculty Publications
Social network analysis has emerged as a key technique in modern sociology, but has recently gained a lot of interest in Web mining research, because of the advent and the increasing popularity of social media, such as blogs, social networks, micro-blogging, customer review sites etc. Such media often serve as platforms for information dissemination and product placement or promotion. One way to improve the quality of recommendations provided to the members of social networks is to use trustworthy resources. In this environment, community-based reputation can help estimating the trustworthiness of individual users. Consequently, influence and trust are becoming essential qualities …
On The Difference In Quality Between Current Heuristic And Optimal Solutions To The Protein Structure Alignment Problem, Mauricio Arriagada, Aleksandar Poleksic
On The Difference In Quality Between Current Heuristic And Optimal Solutions To The Protein Structure Alignment Problem, Mauricio Arriagada, Aleksandar Poleksic
Faculty Publications
The importance of pairwise protein structural comparison in biomedical research is fueling the search for algorithms capable of finding more accurate structural match of two input proteins in a timely manner. In recent years, we have witnessed rapid advances in the development of methods for approximate and optimal solutions to the protein structure matching problem. Albeit slow, these methods can be extremely useful in assessing the accuracy of more efficient, heuristic algorithms. We utilize a recently developed approximation algorithm for protein structure matching to demonstrate that a deep search of the protein superposition space leads to increased alignment accuracy with …
Large-Scale Cooperative Task Distribution On Peer-To-Peer Networks, Daniel R. Karrels, Gilbert L. Peterson, Barry E. Mullins
Large-Scale Cooperative Task Distribution On Peer-To-Peer Networks, Daniel R. Karrels, Gilbert L. Peterson, Barry E. Mullins
Faculty Publications
Large-scale systems are part of a growing trend in distributed computing, and coordinating control of them is an increasing challenge. This paper presents a cooperative agent system that scales to one million or more nodes in which agents form coalitions to complete global task objectives. This approach uses the large-scale Command and Control (C2) capabilities of the Resource Clustered Chord (RC-Chord) Hierarchical Peer-to-Peer (HP2P) design. Tasks are submitted that require access to processing, data, or hardware resources, and a distributed agent search is performed to recruit agents to satisfy the distributed task. This approach differs from others by incorporating design …
Insider Threat Detection Using Virtual Machine Introspection, M. Crawford, Gilbert L. Peterson, Robert F. Mills, Michael R. Grimaila
Insider Threat Detection Using Virtual Machine Introspection, M. Crawford, Gilbert L. Peterson, Robert F. Mills, Michael R. Grimaila
Faculty Publications
This paper presents a methodology for signaling potentially malicious insider behavior using virtual machine introspection (VMI). VMI provides a novel means to detect potential malicious insiders because the introspection tools remain transparent and inaccessible to the guest and are extremely difficult to subvert. This research develops a four step methodology for development and validation of malicious insider threat alerting using VMI. A malicious attacker taxonomy is used to decompose each scenario to aid identification of observables for monitoring for potentially malicious actions. The effectiveness of the identified observables is validated using two data sets. Results of the research show the …