Computer Sciences Commons^™

A Cloud-Based Framework For Smart Permit System For Buildings, Magdalini Eirinaki, Subhankar Dhar, Shishir Mathur

Faculty Publications

In this paper we propose a novel cloud-based platform for building permit system that is efficient, user-friendly, transparent, and has quick turn-around time for homeowners. Compared to the existing permit systems, the proposed smart city permit framework provides a pre-permitting decision workflow, and incorporates a data analytics and mining module that enables the continuous improvement of a) the end user experience, by analyzing explicit and implicit user feedback, and b) the permitting and urban planning process, allowing a gleaning of key insights for real estate development and city planning purposes, by analyzing how users interact with the system depending on …

Pro-Fit: Exercise With Friends, Saumil Dharia, Vijesh Jain, Jvalant Patel, Jainikkumar Vora, Rizen Yamauchi, Magdalini Eirinaki, Iraklis Varlamis

Faculty Publications

The advancements in wearable technology, where embedded accelerometers, gyroscopes and other sensors enable the users to actively monitor their activity have made it easier for individuals to pursue a healthy lifestyle. However, most of the existing applications expect continuous commitment from the end users, who need to proactively interact with the application in order to connect with friends and attain their goals. These applications fail to engage and motivate users who have busy schedules, or are not as committed and self-motivated. In this work, we present PRO-Fit, a personalized fitness assistant application that employs machine learning and recommendation algorithms in …

Threshold-Bounded Influence Dominating Sets For Recommendations In Social Networks, Magdalini Eirinaki, Nuno Moniz, Katerina Potika

Faculty Publications

The process of decision making in humans involves a combination of the genuine information held by the individual, and the external influence from their social network connections. This helps individuals to make decisions or adopt behaviors, opinions or products. In this work, we seek to investigate under which conditions and with what cost we can form neighborhoods of influence within a social network, in order to assist individuals with little or no prior genuine information through a two-phase recommendation process. Most of the existing approaches regard the problem of identifying influentials as a long-term, network diffusion process, where information cascading …

Time-Based Ensembles For Prediction Of Rare Events In News Streams, Nuno Moniz, Luís Torgo, Magdalini Eirinaki

Faculty Publications

Thousands of news are published everyday reporting worldwide events. Most of these news obtain a low level of popularity and only a small set of events become highly popular in social media platforms. Predicting rare cases of highly popular news is not a trivial task due to shortcomings of standard learning approaches and evaluation metrics. So far, the standard task of predicting the popularity of news items has been tackled by either of two distinct strategies related to the publication time of news. The first strategy, a priori, is focused on predicting the popularity of news upon their publication when …

Differential Effects Of Munc18s On Multiple Degranulation-Relevant Trans-Snare Complexes, Hao Xu, Matthew Grant Arnold, Sushmitha Vijay Kumar

Faculty Publications

Mast cell exocytosis, which includes compound degranulation and vesicle-associated piecemeal degranulation, requires multiple Q- and R- SNAREs. It is not clear how these SNAREs pair to form functional trans-SNARE complexes and how these trans-SNARE complexes are selectively regulated for fusion. Here we undertake a comprehensive examination of the capacity of two Q-SNARE subcomplexes (syntaxin3/SNAP-23 and syntaxin4/SNAP-23) to form fusogenic trans-SNARE complexes with each of the four granule-borne R-SNAREs (VAMP2, 3, 7, 8). We report the identification of at least six distinct trans-SNARE complexes under enhanced tethering conditions: i) VAMP2/syntaxin3/SNAP-23, ii) VAMP2/syntaxin4/SNAP-23, iii) VAMP3/syntaxin3/SNAP-23, iv) VAMP3/syntaxin4/SNAP-23, v) VAMP8/syntaxin3/SNAP-23, and vi) VAMP8/syntaxin4/SNAP-23. …

Project Barriers To Green Belts Through Critical Success Factors, Chad Laux, Mary E. Johnson, Paul Cada

Faculty Publications

Purpose The purpose of this paper is to utilize critical success factors (CSF) and identify items Green Belt (GB) practitioners note as barriers to completion of Six Sigma (SS) projects in a major manufacturer setting.

Design/methodology/approach The design of this paper is a descriptive study of a single location of a global manufacturer’s internal data and survey of accredited GBs who have completed an SS project for company accreditation utilizing company focus on CSFs.

Findings The results demonstrate the GB practitioners have competing priorities, have time constraints and lack project management skills that reduce timely completion of SS projects. Top …

A Kansa-Radial Basis Function Method For Elliptic Boundary Value Problems In Annular Domains, Xiao Yan Liu, Andreas Karageorghis, C. S. Chen

Faculty Publications

We employ a Kansa-radial basis function (RBF) method for the numerical solution of elliptic boundary value problems in annular domains. This discretization leads, with an appropriate selection of collocation points and for any choice of RBF, to linear systems in which the matrices possess block circulant structures. These linear systems can be solved efficiently using matrix decomposition algorithms and fast Fourier transforms. A suitable value for the shape parameter in the various RBFs used is found using the leave-one-out cross validation algorithm. In particular, we consider problems governed by the Poisson equation, the inhomogeneous biharmonic equation and the inhomogeneous Cauchy–Navier …

Con-Resistant Trust For Improved Reliability In A Smart Grid Special Protection System, Crystal M. Shipman, Kenneth M. Hopkinson, Juan L. Lopez Jr.

Faculty Publications

This paper applies a con-resistant trust mechanism to improve the performance of a communications-based special protection system to enhance its effectiveness and resiliency. Smart grids incorporate modern information technologies to increase reliability and efficiency through better situational awareness. However, with the benefits of this new technology come the added risks associated with threats and vulnerabilities to the technology and to the critical infrastructure it supports. The research in this paper uses con-resistant trust to quickly identify malicious or malfunctioning (untrustworthy) protection system nodes to mitigate instabilities. The con-resistant trust mechanism allows protection system nodes to make trust assessments based on …

A Function-To-Task Process Model For Adaptive Automation System Design, Jason M. Bindewald, Michael E. Miller, Gilbert L. Peterson

Faculty Publications

Adaptive automation systems allow the user to complete a task seamlessly with a computer performing tasks at which the human operator struggles. Unlike traditional systems that allocate functions to either the human or the machine, adaptive automation varies the allocation of functions during system operation. Creating these systems requires designers to consider issues not present during static system development. To assist in adaptive automation system design, this paper presents the concept of inherent tasks and takes advantage of this concept to create the function-to-task design process model. This process model helps the designer determine how to allocate functions to the …

Seqassist: A Novel Toolkit For Preliminary Analysis Of Next-Generation Sequencing Data, Yan Peng, Andrew S. Maxwell, Natalie D. Barker, Jennifer G. Laird, Alan J. Kennedy, Nan Wang, Chaoyang Zhang, Ping Gong

Faculty Publications

Background: While next-generation sequencing (NGS) technologies are rapidly advancing, an area that lags behind is the development of efficient and user-friendly tools for preliminary analysis of massive NGS data. As an effort to fill this gap to keep up with the fast pace of technological advancement and to accelerate data-to-results turnaround, we developed a novel software package named SeqAssist ("Sequencing Assistant" or SA).

Results: SeqAssist takes NGS-generated FASTQ files as the input, employs the BWA-MEM aligner for sequence alignment, and aims to provide a quick overview and basic statistics of NGS data. It consists of three separate workflows: …

Machine Learning Nuclear Detonation Features, Daniel T. Schmitt, Gilbert L. Peterson

Faculty Publications

Nuclear explosion yield estimation equations based on a 3D model of the explosion volume will have a lower uncertainty than radius based estimation. To accurately collect data for a volume model of atmospheric explosions requires building a 3D representation from 2D images. The majority of 3D reconstruction algorithms use the SIFT (scale-invariant feature transform) feature detection algorithm which works best on feature-rich objects with continuous angular collections. These assumptions are different from the archive of nuclear explosions that have only 3 points of view. This paper reduces 300 dimensions derived from an image based on Fourier analysis and five edge …

Timing Mark Detection On Nuclear Detonation Video, Daniel T. Schmitt, Gilbert L. Peterson

Faculty Publications

During the 1950s and 1960s the United States conducted and filmed over 200 atmospheric nuclear tests establishing the foundations of atmospheric nuclear detonation behavior. Each explosion was documented with about 20 videos from three or four points of view. Synthesizing the videos into a 3D video will improve yield estimates and reduce error factors. The videos were captured at a nominal 2500 frames per second, but range from 2300-3100 frames per second during operation. In order to combine them into one 3D video, individual video frames need to be correlated in time with each other. When the videos were captured …

Epaminondas: Exploring Combat Tactics, David W. King, Gilbert L. Peterson

Faculty Publications

Epaminondas is a two-person, zero-sum strategy game that combines long-term strategic play with highly tactical move sequences. The game has two unique features that make it stand out from other games. The first feature is the creation of phalanxes, which are groups of pieces that can move as a whole unit. As the number of pieces in a phalanx increases, the mobility and capturing power of the phalanx also increases. The second feature differs from many other strategy games: when a player makes a crossing, a winning move in the game, the second player has an opportunity to respond. This …

Querie: Collaborative Database Exploration, Magdalini Eirinaki, Suju Abraham, Neoklis Polyzotis, Naushin Shaikh

Faculty Publications

No abstract provided.

Narratives As A Fundamental Component Of Consciousness, Sandra L. Vaughan, Robert F. Mills, Michael R. Grimaila, Gilbert L. Peterson, Steven K. Rogers

Faculty Publications

In this paper, we propose a conceptual architecture that models human (spatially-temporally-modally) cohesive narrative development using a computer representation of quale properties. Qualia are proposed to be the fundamental "cognitive" components humans use to generate cohesive narratives. The engineering approach is based on cognitively inspired technologies and incorporates the novel concept of quale representation for computation of primitive cognitive components of narrative. The ultimate objective of this research is to develop an architecture that emulates the human ability to generate cohesive narratives with incomplete or perturbated information.

User Identification And Authentication Using Multi-Modal Behavioral Biometrics, Kyle O. Bailey, James S. Okolica, Gilbert L. Peterson

Faculty Publications

Biometric computer authentication has an advantage over password and access card authentication in that it is based on something you are, which is not easily copied or stolen. One way of performing biometric computer authentication is to use behavioral tendencies associated with how a user interacts with the computer. However, behavioral biometric authentication accuracy rates are worse than more traditional authentication methods. This article presents a behavioral biometric system that fuses user data from keyboard, mouse, and Graphical User Interface (GUI) interactions. Combining the modalities results in a more accurate authentication decision based on a broader view of the user's …

Multi-Objective Optimization Of Dead-Reckoning Error Thresholds For Virtual Environments, Jeremy R. Millar, Douglas D. Hodson, Gary B. Lamont, Gilbert L. Peterson

Faculty Publications

Design trade-offs between state consistency and system response time are commonplace in virtual environments. Systems typically rely on predictive consistency algorithms such as dead-reckoning to control consistency and response time. Dead-reckoning error threshold selection determines the consistency/response time trade-off. We extend this trade-off space to explicitly account for the concept of system fairness. We derive a multi-objective optimization problem and apply multi-objective evolutionary algorithms to solve for Pareto optimal error thresholds. Abstract ©2014 IEEE.

A Trust-Aware System For Personalized User Recommendations In Social Networks, Magdalini Eirinaki, Malamati Louta, Iraklis Varlamis

Faculty Publications

Social network analysis has recently gained a lot of interest because of the advent and the increasing popularity of social media, such as blogs, social networking applications, microblogging, or customer review sites. In this environment, trust is becoming an essential quality among user interactions and the recommendation for useful content and trustful users is crucial for all the members of the network. In this paper, we introduce a framework for handling trust in social networks, which is based on a reputation mechanism that captures the implicit and explicit connections between the network members, analyzes the semantics and dynamics of these …

Identification Of Biomarkers That Distinguish Chemical Contaminants Based On Gene Expression Profiles, Xiaomou Wei, Junmei Ai, Youping Deng, Xin Guan, David R. Johnson, Choo Y. Ang, Chaoyang Zhang, Edward J. Perkins

Faculty Publications

Background: High throughput transcriptomics profiles such as those generated using microarrays have been useful in identifying biomarkers for different classification and toxicity prediction purposes. Here, we investigated the use of microarrays to predict chemical toxicants and their possible mechanisms of action.

Results: In this study, in vitro cultures of primary rat hepatocytes were exposed to 105 chemicals and vehicle controls, representing 14 compound classes. We comprehensively compared various normalization of gene expression profiles, feature selection and classification algorithms for the classification of these 105 chemicals into14 compound classes. We found that normalization had little effect on the averaged …

Applicability Of Latent Dirichlet Allocation To Multi-Disk Search, George E. Noel, Gilbert L. Peterson

Faculty Publications

Digital forensics practitioners face a continual increase in the volume of data they must analyze, which exacerbates the problem of finding relevant information in a noisy domain. Current technologies make use of keyword based search to isolate relevant documents and minimize false positives with respect to investigative goals. Unfortunately, selecting appropriate keywords is a complex and challenging task. Latent Dirichlet Allocation (LDA) offers a possible way to relax keyword selection by returning topically similar documents. This research compares regular expression search techniques and LDA using the Real Data Corpus (RDC). The RDC, a set of over 2400 disks from real …

How Well Does Multiple Ocr Error Correction Generalize?, William B. Lund, Eric K. Ringger, Daniel D. Walker

Faculty Publications

As the digitization of historical documents, such as newspapers, becomes more common, the need of the archive patron for accurate digital text from those documents increases. Building on our earlier work, the contributions of this paper are: 1. in demonstrating the applicability of novel methods for correcting optical character recognition (OCR) on disparate data sets, including a new synthetic training set, 2. enhancing the correction algorithm with novel features, and 3. assessing the data requirements of the correction learning method. First, we correct errors using conditional random fields (CRF) trained on synthetic training data sets in order to demonstrate the …

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 …

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

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

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 …

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 …

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 …

Effects Of Stereo And Screen Size On The Legibility Of Three-Dimensional Streamtube Visualization, Jian Chen, Haipeng Cai, Alexander P. Auchus, David H. Laidlaw

Faculty Publications

We report the impact of display characteristics (stereo and size) on task performance in diffusion magnetic resonance imaging (DMRI) in a user study with 12 participants. The hypotheses were that (1) adding stereo and increasing display size would improve task accuracy and reduce completion time, and (2) the greater the complexity of a spatial task, the greater the benefits of an improved display. Thus we expected to see greater performance gains when detailed visual reasoning was required. Participants used dense streamtube visualizations to perform five representative tasks: (1) determine the higher average fractional anisotropy (FA) values between two regions, (2) …

Eliciting A Sensemaking Process From Verbal Protocols Of Reverse Engineers, Adam R. Bryant, Robert F. Mills, Gilbert L. Peterson, Michael R. Grimaila

Faculty Publications

A process of sensemaking in reverse engineering was elicited from verbal protocols of reverse engineers as they investigated the assembly code of executable programs. Four participants were observed during task performance and verbal protocols were collected and analyzed from two of the participants to determine their problem-solving states and characterize likely transitions between those states. From this analysis, a high-level process of sensemaking is described which represents hypothesis generation and information-seeking behaviors in reverse engineering within a framework of goal-directed planning. Future work in validation and application of the process is discussed.

The Complexity Of Pebbling In Diameter Two Graphs*, Charles A. Cusack, Timothy Lewis, Daniel Simpson, Samuel Taggart

Faculty Publications

Given a simple, connected graph, a pebbling configuration is a function from its vertex set to the nonnegative integers. A pebbling move between adjacent vertices removes two pebbles from one vertex and adds one pebble to the other. A vertex r is said to be reachable from a configuration if there exists a sequence of pebbling moves that places one pebble on r. A configuration is solvable if every vertex is reachable. We prove tight bounds on the number of vertices with two and three pebbles that an unsolvable configuration on a diameter two graph can have in terms of …