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  Partner: UNT College of Engineering
 Collection: UNT Scholarly Works
Secure execution environments through reconfigurable lightweight cryptographic components
This doctoral dissertation discusses secure execution environments through reconfigurable lightweight cryptographic components. The author considers the four most important dimensions of software protection.
WARM SRAM: A Novel Scheme to Reduce Static Leakage Energy in SRAM Arrays
This presentation accompanies a paper discussing research on a novel scheme to reduce static leakage energy in SRAM arrays.
Amazon Mechanical Turk for Subjectivity Word Sense Disambiguation
In this paper, the authors discuss research on whether they can use Mechanical Turk (MTurk) to acquire goo annotations with respect to gold-standard data, whether they can filter out low-quality workers (spammers), and whether there is a learning effect associated with repeatedly completing the same kind of task.
An Algorithm for Open Text Semantic Parsing
Abstract: This paper describes an algorithm for open text shallow semantic parsing. The algorithm relies on a frame dataset (FrameNet) and a semantic network (WordNet), to identify semantic relations between words in open text, as well as shallow semantic features associated with concepts in the text. Parsing semantic structures allows semantic units and constituents to be accessed and processed in a more meaningful way than syntactic parsing, moving the automation of understanding natural language text to a higher level.
Real-Time Systems: An Introduction and the State-of-the-Art
This encyclopedia article gives an overview of the broad area of real-time systems. This task is daunting because real-time systems are everywhere, and yet no generally accepted definition differentiates real-time systems from non-real-time systems. The authors make an attempt at providing a general overview of the different classes of real-time systems, scheduling of tasks (or threads) in such systems, design tools and environments for real-time systems, real-time operating systems, and embedded systems. The authors conclude their discussion with research challenges that still remain.
Modeling and Analysis Using Computational Tools
This book chapter presents two special algorithms, Mean Value Analysis and Convolution Algorithm, for the analysis of closed queuing networks, and an introduction to simulation techniques that are widely used in analyzing queuing systems in general. In the illustration of special algorithms, the authors use simplifying assumptions that also show how they provide practical solutions to systems that are interactable or when their behaviors cannot be easily modeled using simple probability distributions.
Anchor Nodes Placement for Effective Passive Localization
This paper discusses anchor nodes placement for effective passive localization. The authors show that, for effective passive localization, the optimal placement of the anchor nodes is at the center of the network in such a way that no three anchor nodes share linearity.
Attracting and Retaining Women in Computer Science and Engineering: Evaluating the Results
This paper discusses efforts to attract and retain students in computer science and engineering fields.
Virtualization Based Secure Execution And Testing Framework
This article discusses virtualization based secure execution and testing.
Classifying genes to the correct Gene Ontology Slim term in Saccharomyces cerevisiae using neighbouring genes with classification learning
This article discusses classifying genes to the correct Gene Ontology Slim term in Saccharomyces cerevisiae using neighbouring genes with classification learning. This provides insight into a gene's functionality in the eukaryotic genome.
AC 2007-1844: An Innovative Mechanical and Energy Engineering Curriculum
This paper discusses the addition of a new Department of Mechanical and Energy Engineering at the University of North Texas (UNT). Those involved see the curriculum for this new program as a new model of engineering education that parallels the innovations of UNTs current Learning to Learn (L2L) project-oriented concept course with the addition of innovative approaches for mechanical engineering and emphasis on energy engineering education.
Transformational Paradigm for Engineering and Engineering Technology Education
This paper discusses a transformational paradigm for engineering and engineering technology education at the baccalaureate level.
Hybrid Approach for Energy-Aware Synchronization
This book chapter discusses a time synchronization scheme for wireless sensor networks that aims to save sensor battery power while maintaining network connectivity for as long as possible. It focuses on aspects of wireless sensor networks. These include designing a hybrid method between reference broadcast synchronization (RBS) and timing-sync protocol for sensor networks (TPSN) to reduce the number of transmissions required to synchronize an entire network, extending single-hop synchronization methods to operate in large multi-hop networks, verifying that the hybrid methods operate as desired by simulating against RBS and TPSN, and maintaining network connectivity and coverage.
Tantra: A fast PRNG algorithm and its implementation
This paper discusses Tantra. Tantra is a novel Pseudorandom number generator (PRNG) design that provides a long sequence high quality pseudorandom numbers at very high rate both in software and hardware implementations.
The Decomposition of Human-Written Book Summaries
In this paper, the authors evaluate the extent to which human-written book summaries can be obtained through cut-and-paste operations from the original book. The authors analyze the effect of the parameters involved in the decomposition algorithm, and highlight the distinctions in coverage obtained for different summary types.
Building a Sense Tagged Corpus with Open Mind Word Expert
This paper discusses building a sense tagged corpus with Open Mind Word Expert. Abstract: Open Mind Word Expert is an implemented active learning system for collecting word sense tagging from the general public over the Web. It is available at The authors expect the system to yield a large volume of high-quality training data at a much lower cost than the traditional method of hiring lexicographers. The authors thus propose a Senseval-3 lexical sample activity where the training data is collected via Open Mind Word Expert. If successful, the collection process can be extended to create the definitive corpus of word sense information.
Co-training and Self-training for Word Sense Disambiguation
This paper investigates the application of co-training and self-training to word sense disambiguation. Optimal and empirical parameter selection methods for co-training and self-training are investigated, with various degrees of error reduction. A new method that combines co-training with majority voting is introduced, with the effect of smoothing the bootstrapping learning curves, and improving the average performance.
Open Mind Word Expert: Creating Large Data Collections with Web Users' Help
This article discusses Open Mind Word Expert (OMWE). The World Wide Web has both exacerbated the need and provided an opportunity for creating automatic tools for language processing. OMWE is a system that aims to tap people's ability to disambiguate words and to give computers the benefit of people's knowledge. Any Web user can visit the OMWE site and contribute some knowledge about the meanings of given words in given sentences. As a result, OMWE creates large sense-tagged corpora that can be used to build automatic WSD systems.
Efficient Energy Saving Scheme for On-Chip Caches
This paper discusses efficient energy saving techniques for on-chip caches, focusing especially on drowsy cache schemes.
Evaluation Results of an E and ET Education Forum
This article discusses evaluation results of an Engineering (E) and Engineering Technology (ET) education forum at the University of Houston. A central focus to these discussions revolved around whether Engineering and Engineering Technology exist as separate fields or whether there was value in thinking about them as part of a continuum.
Answering complex, list and context questions with LCC's Question-Answering Server
This paper presents the architecture of the Question-Answering server (QAS) developed at the Language Computer Corporation (LCC) and used in the TREC-10 evaluations.
Effects of Call Arrival Rate and Mobility on Network Throughput in Multi-Cell CDMA
This presentation discusses call arrival rate and mobility. The effect of call arrival rate on the capacity of a code-division multiple-access (CDMA) cellular network is evaluated. First the inter-cell and intra-cell interferences of every cell on every other cell are calculated for a given network topology. Then the capacity region for the number of simultaneous calls in every cell is defined for specified system parameters. This region is used to evaluate the new call blocking and handoff call blocking probabilities.
Characterizing Humour: An Exploration of Features in Humorous Texts
This paper investigates the problem of automatic humor recognition, and provides an in-depth analysis of two of the most frequently observed features of humorous text: human-centeredness and negative polarity. Through experiments performed on two collections of humorous texts, the authors show that these properties of verbal humor are consisted across different data sets.
Computational Models for Incongruity Detection in Humour
This paper discusses computational models for incongruity resolution. Abstract: Incongruity resolution is one of the most widely accepted theories of humor, suggesting that humor is due to the mixing of two disparate interpretation frames in one statement. In this paper, the authors explore several computational models for incongruity resolution. The authors introduce a new data set, consisting of a series of 'set-ups' (preparations for a punch line), each of them followed by four possible coherent continuations out of which only one has a comic effect. Using this data set, the authors redefine the task as the automatic identification of the humorous punch line among all the plausible endings. The authors explore several measures of semantic relatedness, along with a number of joke-specific features, and try to understand their appropriateness as computational models for incongruity detection.
Retention and Recruitment of Women in Computer Engineering
This presentation discusses strategies and goals for recruiting more women to Computer Science and Engineering degree (CSE) programs at the University of North Texas (UNT). It also describes a series of activities aimed at improving retention rates of women students already in our programs. Such recruitment and retention of women is critical to the country's efforts to increase the number of engineering professionals, and is a priority for the CSE Department at UNT.
Research, Teaching, and Outreach
This presentation discusses research in sensor network routing, WiFi network optimization, 3G cellular call admission, and the outreach and resources needed.
Robocamp: Encouraging Young Women to Embrace STEM
This presentation discusses Robocamp, a special summer camp that was created by the University of North Texas (UNT) Computer Science and Engineering department. Robocamp successfully promotes engineering among high school women.
WiFi and WCDMA Network Design
This presentation discusses WiFi access point selection and traffic balancing, multi-cell wideband code division multiple access (WCDMA) with multiple classes, user modeling using 2D Gaussian distribution, and intra-cell and inter-cell interference and capacity.
How to Hide Secrets from Operating System: Architecture Level Support for Dynamic Address Trace Obfuscation
This technical report addresses how to hide secrets from an operating system. The authors provide a detailed design for the VM blackbox and some microarchitecture level simulation derived performance data. They also describe a compiler directed prefetch scheme that uses both instruction and data prefetches to obfuscate the address traces on the address bus between on-chip L2 cache and memory.
Hybrid Energy-Aware Synchronization Algorithm in Wireless Sensor Networks
This paper discusses a time synchronization scheme for wireless sensor networks that aims to conserve sensor battery power while maintaining network connectivity for as long as possible.
Grid-based Coordinated Routing in Wireless Sensor Networks
This paper discusses grid-based coordinated routing in wireless sensor networks and compares the energy available in the network over time for different grid sizes. The authors explore the quality of service of wireless sensor networks, how the coordinator nodes are elected, and the size of the grid area that will minimize the total energy consumption and extend the lifetime of the network.
Multicell CDMA Network Design
This article discusses multicell CDMA network design.
A Non-Preemptive Scheduling Algorithm for Soft Real-Time Systems
This article discusses a non-preemptive scheduling algorithm for soft real-time systems.
Cell Design to Maximize Capacity in CDMA Networks
This presentation discusses the code division multiple access (CDMA) inter-cell effects, capacity regions, maximizing network capacity, mobility, a call admission control algorithm, and network performance.
CDMA Network Design
This presentation gives an overview of code-division multiple access (CDMA) and inter-cell effects, network capacities, sensitivity analysis of base station locations, pilot-signal power, and transmission power of the mobiles, and concludes with numerical results.
Cell Placement in a CDMA Network
This presentation discusses research on cell placement in a CDMA network. In order to enable iterative cell placement the authors use a computationally efficient iterative process to calculate the inter-cell and intra-cell interferences as a function of pilot-signal power and base station location.
CCAP: A Strategic Tool for Managing Capacity of CDMA Networks
This presentation discusses CCAP, a strategic tool for managing capacity of CDMA networks. CCAP is a graphical interactive tool for CDMA that calculates the coverage area, call capacity of a CDMA network, and subscriber network performance to optimize capacity.
Capacity Allocations in Multi-cell UMTS Networks for Different Spreading Factors with Perfect and Imperfect Power Control
This presentation discusses user and interference models, wideband code division multiple access (WCDMA) capacity with perfect and imperfect power control, and spreading factors with numerical results.
Call Admission Control Scheme for Arbitrary Traffic Distribution in CDMA Cellular Systems
This presentation discusses call admission control (CAC). The authors define a set of feasible call configurations that results in a CAC algorithm that captures the effect of having an arbitrary traffic distribution and whose complexity scales linearly with the number of cells.
Global versus Local Call Admission Control in CDMA Cellular Networks
This presentation discusses interference model impacts on capacity, global call admission controls, local call admission controls, and the differences in global versus local call admission controls.
Impact of Interference Model on Capacity in CDMA Cellular Networks
This presentation introduces code division multiple access (CDMA) networks, average and actual interference models, optimized capacity, and the 2D Gaussian user model.
Subscriber Maximization in CDMA Cellular Networks
This presentation gives an overview of code division multiple access (CDMA), traffic and mobility models, subscriber optimization formulation, and numerical results.
Text-to-text Semantic Similarity for Automatic Short Answer Grading
In this paper, the authors explore unsupervised techniques for the task of automatic short answer grading. The authors compare a number of knowledge-based and corpus-based measures of text similarity, evaluate the effect of domain and size on the corpus-based measures, and also introduce a novel technique to improve the performance of the system by integrating automatic feedback from the student answers. Overall, our system significantly and consistently outperforms other unsupervised methods for short answer grading that have been proposed in the past.
Quantifying the Limits and Success of Extractive Summarization Systems Across Domains
This paper analyzes the topic identification stage of single-document automatic text summarization across four different domains, consisting of newswire, literary, scientific and legal documents. The authors present a study that explores the summary space of each domain via an exhaustive search strategy, and finds the probability density function (pdf) of the ROUGE score distributions for each domain. The authors then use this pdf to calculate the percentile rank of extractive summarization systems. Their results introduce a new way to judge the success of automatic summarization systems and bring quantified explanations to questions such as why it was so hard for the systems to date to have a statistically significant improvement over the lead baseline in the news domain.
Word Alignment for Languages with Scarce Resources
This paper presents the task definition, resources, participating systems, and comparative results for the shared task on word alignment which was organized as part of the Association for Computational Linguistics (ACL) 2005 Workshop on Building and Using Parallel Texts. The shared task included English-Inuktitut, Romanian-English, and English-Hindi sub-tasks, and drew the participation of ten teams from around the world with a total of 50 systems.
UNT: SubFinder: Combining Knowledge Sources for Automatic Lexical Substitution
Abstract: This paper describes the University of North Texas SubFinder system. The system is able to provide the most likely set of substitutes for a word in a given context, by combining several techniques and knowledge sources. SubFinder has successfully participated in the best and out of ten (oot) tracks in the SEMEVAL lexical substitution task, consistently ranking in the first or second place.
SemEval-2007 Task 14: Affective Text
This paper discusses affective text. Abstract: The "Affective Text" task focuses on the classification of emotions and valence (positive/negative polarity) in news headlines, and is meant as an exploration of the connection between emotions and lexical semantics. In this paper, the authors describe the data set used in the evaluation and the results obtained by the participating systems.
Putting Pieces Together: Combining FrameNet, VerbNet and WordNet for Robust Semantic Parsing
This paper describes the authors' work in integrating three different lexical resources: FrameNet, VerbNet, and WordNet, into a unified, richer knowledge-base, to the end of enabling more robust semantic parsing. The construction of each of these lexical resources has required many years of laborious human effort, and they all have their strengths and shortcomings. By linking them together, the authors build an improved resource in which (1) the coverage of FrameNet is extended, (2) the VerbNet lexicon is augmented with frame semantics, and (3) selectional restrictions are implemented using WordNet semantic classes. The synergistic exploitation of various lexical resources is crucial for many complex language processing applications, and the authors prove it once again effective in building a robust semantic parser.
Random-Walk Term Weighting for Improved Text Classification
This paper describes a new approach for estimating term weights in a document, and shows how the new weighting scheme can be used to improve the accuracy of a text classifier. The method uses term co-occurrence as a measure of dependency between word features. A random-walk model is applied on a graph encoding works and co-occurence dependencies, resulting in scores that represent a quantification of how a particular word feature contributes to a given context. Experiments performed on three standard classification datasets show that the new random-walk based approach outperforms the traditional term frequency approach of feature weighting.
Topic Identification Using Wikipedia Graph Centrality
This paper presents a method for automatic topic identification using a graph-centrality algorithm applied to an encyclopedic graph derived from Wikipedia. When tested on a data set with manually assigned topics, the system is found to significantly improve over a simpler baseline that does not make use of the external encyclopedic knowledge.