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- AC 2007-1844: An Innovative Mechanical and Energy Engineering Curriculum
- This paper discusses Mechanical and Energy Engineering curriculum. Abstract: The continuing expansion of the new College of Engineering at the University of North Texas (UNT) has created an opportunity to establish a new Department of Mechanical and Energy Engineering and an excellent occasion for the establishment of innovative and interdisciplinary approaches to engineering education. The explicit addition of Energy to the Mechanical Engineering curriculum is a new model of engineering education that parallels the innovations of our current Learning to Learn (L2L) project oriented concept course with the addition of innovative approaches for mechanical engineering and emphasis on energy engineering education. The new Mechanical and Energy Engineering (MEE) baccalaureate-level program will provide the intellectual foundation for successful career preparation and lifelong learning for the students. This innovative curriculum has been designed with a system-level approach to ME-based design., on the fundamentals of undergraduate level energy engineering within the mechanical engineering discipline, and will provide experiential-oriented approaches for the better understanding of classical mechanical engineering principles. It will also provide a new interdisciplinary ME curriculum approach to the most important energy technology areas. We are going to present the curriculum and discuss components of the program from freshman to to senior years. We expect that the graduates of this innovative undergraduate curriculum in Mechanical and Energy Engineering will have a unique educational experience with systems integration approach for addressing industrial challenges; working on interdisciplinary teams; and with cognitive learning experiences for responsible lifelong learning, in order to sustain creativity and productivity in their careers. digital.library.unt.edu/ark:/67531/metadc67616/
- 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. digital.library.unt.edu/ark:/67531/metadc30953/
- Amazon Mechanical Turk for Subjectivity Word Sense Disambiguation
- This paper discusses word sense disambiguation. Abstract: Amazon Mechanical Turk (MTurk) is a marketplace for so-called "human intelligence tasks" (HITs), or tasks that are easy for humans but currently difficult for automated processes. Providers upload tasks to MTurk which workers then complete. Natural language annotation is one such human intelligence task. In this paper, the authors investigate using MTurk to collect annotations for Subjectivity Word Sense Disambiguation (SWSD), a course-grained word sense disambiguation task. The authors investigate whether they can use MTurk to acquire good 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. While our results with respect to spammers are inconclusive, the authors are able to obtain high-quality annotations for the SWSD task. These results suggest a greater role for MTurk with respect to constructing a large scale SWSD system in the future, promising substantial improvement in subjectivity and sentiment analysis. digital.library.unt.edu/ark:/67531/metadc31023/
- Anchor Nodes Placement for Effective Passive Localization
- This paper discusses anchor nodes placement for effective passive localization. Abstract: In many applications, the exact location of the sensor nodes is unknown after deployment. Localization is a process used to locate sensor nodes' positional coordinates, which is vital information. The localization is generally assisted by anchor nodes that are also sensor nodes but with known locations. Anchor nodes generally are expensive and need to be optimally placed for effective localization. Passive localization is one of the localization techniques where the sensor nodes silently listen to the global events like thunder sounds, seismic waves, lighting, etc. According to previous studies, the ideal location to place anchor nodes was on the perimeter of the sensor network. This may not be the case in passive localization, since the function of anchor nodes here is different than the anchor nodes used in other localization systems. The authors do extensive studies on positioning anchor nodes for effective localization. Several simulations are run in dense and sparse networks for proper positioning of anchor nodes. 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. The more the non-linearity, the better the localization. The localization for our network design proves better when the authors place anchor nodes to form right angles. digital.library.unt.edu/ark:/67531/metadc77116/
- Answering complex, list and context questions with LCC's Question-Answering Server
- Abstract: 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. LCC's QAS™ extracts answers for (a) factual questions of variable degree of difficulty; (b) questions that expect lists of answers; and (c) questions posed in the context of previous questions and answers. One of the major novelties is the implementation of bridging inference mechanisms that guide the search for answers to complex questions. Additionally, LCC's QAS™ encodes an efficient way of modeling context via reference resolution. In TREC-10, this system generated an RAR of 0.58 on the main task and 0.78 on the context task. digital.library.unt.edu/ark:/67531/metadc83297/
- Approximating User Distributions in WCDMA Networks Using 2-D Gaussian
- This paper discusses approximating user distributions in WCDMA networks using 2-D Gaussian. Abstract: In this paper, we present an analytical model for approximating the user distributions in multi-cell third generation WCDMA networks using 2-dimensional Gaussian distributions by determining the means and the standard deviations of the distributions for every cell. This allows us to calculate the inter-cell interference and the reverse-link capacity of the network. The authors compare their model with simulation results and show that it is fast and accurate enough to be used efficiently in the planning process of large WCDMA networks. digital.library.unt.edu/ark:/67531/metadc30820/
- 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. Abstract: Computer science and engineering communities have been exploring a variety of activities and techniques to attract and retain more students, especially women and minorities, to computer science and computer engineering degree programs. This paper briefly describes the efforts and results of a plan for actively recruiting young women into undergraduate computer engineering and computer science programs hosted by the University of North Texas (UNT). It also describes a series of activities aimed at improving the retention rate of students already in our programs, particularly during the freshman year. Such recruitment and retention efforts are critical to the country's efforts to increase the number of engineering professionals, and are a priority for the Computer Science and Engineering (CSE) Department at UNT. digital.library.unt.edu/ark:/67531/metadc30834/
- Automatic generation of a coarse grained WordNet
- This paper discusses automatic generation of a coarse grained WordNet. Abstract: Several principles for the automatic transformation of WordNet into a coarser grained dictionary are proposed. A new version of WordNet is derived, leading to a reduction of 26% in the average polysemy of words, while introducing a small error rate of 2.1%, as measured on a sense tagged corpus. digital.library.unt.edu/ark:/67531/metadc83310/
- Automatic Keyword Extraction for Learning Object Repositories
- Abstract: This paper describes experiments in metadata generation for learning object repositories. Specifically, the authors present several methods for automatic keyword extraction and evaluate them on a collection of learning objects from an undergraduate history course. The results suggest that automatic keyword extraction is a viable solution for suggesting terms and phrases for metadata annotation. digital.library.unt.edu/ark:/67531/metadc31003/
- An Automatic Method for Generating Sense Tagged Corpora
- This paper discusses an automatic method for generating sense tagged corpora. Abstract: The unavailability of very large corpora with semantically disambiguated words is a major limitation in text processing research. For example, statistical methods for word sense disambiguation of free text are known to achieve high accuracy results when large corpora are available to develop context rules, to train and test them. This article presents a novel approach to automatically generate arbitrarily large corpora for word senses. The method is based on (1) the information provided in WordNet, used to formulate queries consisting of synonyms or definitions of word senses, and (2) the information gathered from Internet using existing search engines. The method was tested on 120 word senses and a precision of 91% was observed. digital.library.unt.edu/ark:/67531/metadc83300/
- BABYLON Parallel Text Builder: Gathering Parallel Texts for Low-Density Languages
- This paper discusses BABYLON parallel text builder. Abstract: This paper describes BABYLON, a system that attempts to overcome the shortage of parallel texts in low-density languages by supplementing existing parallel texts with texts gathered automatically from the Web. In addition to the identification of entire Web pages, the authors also propose a new feature specifically designed to find parallel text chunks within a single document. Experiments carried out on the Quechua-Spanish language pair show that the system is successful in automatically identifying a significant amount of parallel texts on the Web. Evaluations of a machine translation system trained on this corpus indicate that the Web-gathered parallel texts can supplement manually compiled parallel texts and perform significantly better than the manually compiled texts when tested on other Web-gathered data. digital.library.unt.edu/ark:/67531/metadc31004/
- A Bootstrapping Method for Building Subjectivity Lexicons for Languages with Scarce Resources
- Abstract: This paper introduces a method for creating a subjectivity lexicon for languages with scarce resources. The method is able to build a subjectivity lexicon by using a small seed set of subjectivity words, and online dictionary, and a small raw corpus, coupled with a bootstrapping process that ranks new candidate words based on a similarity measure. Experiments performed with a rule-based sentence level subjectivity classifier show an 18% absolute improvement in F-measure as compared to previously proposed semi-supervised methods. digital.library.unt.edu/ark:/67531/metadc31002/
- Building a Sense Tagged Corpus with Open Mind Word Expert
- This paper discusses Open Mind Word Expert, an implemented active learning system for collecting word sense tagging from the general public over the Web. It is available at http://teach-computers.org. 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. digital.library.unt.edu/ark:/67531/metadc81389/
- Building Multilingual Semantic Networks with Non-Expert Contributions over the Web
- This paper discusses building multilingual semantic networks. Abstract: We present a system that allows non-expert Web users to contribute towards building a multilingual lexical resource. Our study focuses on the Romanian-English language pair, and the target resource is a Romanian WordNet strongly connected to the English WordNet. We use a bilingual dictionary, a monolingual definition dictionary and documents on the Web to build synsets, attach them a gloss, and provide some examples. The results of the semi-automatic acquisition system are judged by two human judges, and they are compared to automatic approaches to building a Romanian WordNet. digital.library.unt.edu/ark:/67531/metadc30947/
- Capacity Allocations in Multi-cell UMTS Networks for Different Spreading Factors with Perfect and Imperfect Power Control
- This paper discusses capacity allocation in multi-cell UMTS networks. Abstract: An analytical model for calculating capacity in multi-cell UMTS networks is presented. Capacity is maximized for different spreading factors and for perfect and imperfect power control. An analytical model is presented for approximating the user distributions in multi-cell third generation WCDMA networks using 2-dimensional Gaussian distributions by determining the means and the standard deviations of the distribution for every cell. This allows for the calculation of the inter-cell interference and the reverse-link capacity of the network. The capacity was determined for signal-to-interference threshold from 5 dB to 10 dB and spreading factor values of 256, 64, 16, and 4. digital.library.unt.edu/ark:/67531/metadc30825/
- Channel Assignment and Load Distribution in a Power-Managed WLAN
- This paper discusses a proposed algorithm. Abstract: For a Wireless Local Area Network (WLAN), the authors propose an algorithm based on power management of Access Points (APs) to improve load distribution and provide an improved channel assignment. The authors formulate an algorithm that adjusts the transmitted power of the beacon packets of the Most Congested Access Point (MCAP). The transmitted power of the data packets is not altered thus avoiding auto-rating. The algorithm then determines a user assignment that distributes the load efficiently. Finally, the authors apply a channel assignment algorithm to each AP with the objective of minimizing the total interference over the WLAN. Results show that the proposed algorithm is capable of significantly reducing the congestion at the MCAPs, providing better load distribution, and enhancing channel assignment. digital.library.unt.edu/ark:/67531/metadc30835/
- Channel Assignment in an IEEE 802.11 WLAN Based on Signal-to-Interference Ratio
- Abstract: In this paper, we propose a channel-assignment algorithm at the Access Points (APs) of a Wireless Local Area Network (WLAN) in order to maximize Signal-to-Interference Ratio (SIR) at the user level. It begins with the channel assignment at the APs, which is based on minimizing the total interference between APs. Based on this initial assignment, the authors calculate the SIR for each user. The algorithm can be applied to any WLAN, irrespective of the user distribution and user load. Results show that the proposed algorithm is capable of significantly increasing the SIR over the WLAN, which in turn improves throughput. digital.library.unt.edu/ark:/67531/metadc30844/
- 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. digital.library.unt.edu/ark:/67531/metadc30988/
- Classification of Attributes and Behavior in Risk Management Using Bayesian Networks
- This paper discusses issues in security. Abstract: Security administration is an uphill task to implement in an enterprise network providing secured corporate services. With the slew of patches being released by network component vendors, system administrators require a barrage of tools for analyzing the risk due to vulnerabilities in those components. In addition, criticalities in patching some end hosts raises serious security issues about the network to which the end hosts are connected. In this context, it would be imperative to know the risk level of all critical resources keeping in view the everyday emerging new vulnerabilities. The authors hypothesize that sequence of network actions by attackers depends on their social and attack profile (behavioral resources such as skill level, time, and attitude). To estimate the types of attack behavior, the athors surveyed individuals for their ability and attack intent. Using the individuals' responses, the authors determined their behavioral resources and classified them as having opportunist, hacker, or explorer behavior. The profile behavioral resources can be used for determining risk by an attacker having that profile. Thus, suitable vulnerability analysis and risk management strategies can be formulated to efficiently curtail the risk from different types of attackers. digital.library.unt.edu/ark:/67531/metadc30836/
- Classifier Stacking and Voting for Text Filtering
- Abstract: This paper summarizes the approach and the results of the TextCat system participating in the Filtering track in the Text Retrieval Conference 2002. The system relies primarily on statistical methods, and was designed with the main purpose of having a backbone system in which we can further integrate semantic components, and evaluate their relative performance as compared to traditional statistical approaches. They system is therefore simple, and is based on techniques for keywords extraction, and various classifier combinations including stacking and voting. TextCat participated in the Batch and Routing tasks. In the Batch task, it achieved a score of 39.02% normalized utility, and 26.37% F-measure respectively, averaged over all topics. The averaged uninterpolated precision for our best routing submission was 14.16%. digital.library.unt.edu/ark:/67531/metadc30942/
- 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. digital.library.unt.edu/ark:/67531/metadc30955/
- Combining Lexical Resources for Contextual Synonym Expansion
- This paper discusses combining lexical resources for contextual synonym expansion. Abstract: In this paper, we experiment with the task of contextual synonym expansion, and compare the benefits of combining multiple lexical resources using both unsupervised and supervised approaches. Overall, the results obtained through the combination of several resources exceed the current state-of-the-art when selecting the best synonym for a given target word, and place second when selecting the top ten synonyms, thus demonstrating the usefulness of the approach. digital.library.unt.edu/ark:/67531/metadc31011/
- Computational Laughing: Automatic Recognition of Humorous One-liners
- This paper discusses automatic recognition of humor. Abstract: Humor is one of the most interesting and puzzling aspects of human behavior. Despite the attention it has received in fields such as philosophy, linguistics, and psychology, there have been only few attempts to create computational models for humor recognition or generation. In this paper, the authors bring empirical evidence that computational approaches can be successfully applied to the task of humor recognition. Through experiments performed on very large data sets, the authors show that automatic classification techniques can be effectively used to distinguish between humorous and non-humorous texts, with significant improvements observed over apriori known baselines. digital.library.unt.edu/ark:/67531/metadc30966/
- 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. digital.library.unt.edu/ark:/67531/metadc31024/
- Corpus-based and Knowledge-based Measures of Text Semantic Similarity
- Abstract: This paper presents a method for measuring the semantic similarity of texts, using corpus-based and knowledge-based measures of similarity. Previous work on this problem has focused mainly on either large documents (e.g. text classification, information retrieval) or individual words (e.g. synonymy tests). Given that a large fraction of the information available today, on the Web and elsewhere, consists of short text snippets (e.g. abstracts of scientific documents, image captions, product descriptions), in this paper the authors focus on measuring the semantic similarity of short texts. Through experiments performed on a paraphrase data set, the authors show that the semantic similarity method out-performs methods based on simple lexical matching, resulting in up to 13% error rate reduction with respect to the traditional vector-based similarity metric. digital.library.unt.edu/ark:/67531/metadc30981/
- A Corpus-based Approach to Finding Happiness
- This paper discusses how to locate emotions. Abstract: What are the sources of happiness and sadness in everyday life? In this paper, the authors employ 'linguistic ethnography' to seek out where happiness lies in our everyday lives by considering a corpus of blogposts from the LiveJournal community annotated with happy and sad moods. By analyzing this corpus, the authors derive lists of happy and sad words and phrases annotated by their 'happiness factor'. Various semantic analyses performed with this wordlist reveal the happiness trajectory of a 24-day (3am and 9-10p are most happy), and a 7-day week (Wednesdays are saddest), and compare the socialness and human-centeredness of happy descriptions versus sad descriptions. The authors evaluate our corpus-based approach in a classification task and contrast our wordlist with emotionally-annotated wordlists produced by experimental focus groups. Having located happiness temporally and semantically within this corpus of everyday life, the paper concludes by offering a corpus-inspired livable recipe for happiness. digital.library.unt.edu/ark:/67531/metadc30980/
- Creating a Testbed for the Evaluation of Automatically Generated Back-of-the-book Indexes
- This paper discusses automatic generating of back-of-the-book indexes. Abstract: The automatic generation of back-of-the-book indexes seems to be out of sight of the Information Retrieval and Natural Language Processing communities, although the increasingly large number of books available in electronic format, as well as recent advances in key-phrase extraction, should motivate an increased interest in this topic. In this paper, the authors describe the background relevant to the process of creating back-of-the-book indexes, namely (1) a short overview of the origin and structure of back-of-the-book indexes, and (2) the correspondence that can be established between techniques for automatic index construction and keyphrase extraction. Since the development of any automatic system requires in the first place an evaluation testbed, the authors describe their work in building a gold standard collection of books and indexes, and the authors present several metrics that can be used for the evaluation of automatically generated indexes against the gold standard. Finally, the authors investigate the properties of the gold standard index, such as index size, length of index entries, and upper bounds on coverage as indicated by the presence of index entries in the document. digital.library.unt.edu/ark:/67531/metadc30982/
- Creating Large Annotated Data Collections with Web Users' Help
- This paper discusses creating annotated data collections. Abstract: Open Mind Word Expert is an implemented active learning system that aims to create large annotated corpora by tapping into the world's vast pool of knowledge. It does this by relying on the vast number of Web users who contribute their knowledge to data annotation. Open Mind Word Expert focuses on building semantically annotated corpora, by collecting word sense tagging from the general public over the Web. During the first nine months of activity, the system yielded 90,000 high quality tagged items at a much lower cost than the traditional method of hiring lexicographers. digital.library.unt.edu/ark:/67531/metadc30949/
- Cross-lingual Semantic Relatedness Using Encyclopedic Knowledge
- This paper discusses cross-lingual semantic relatedness using encyclopedic knowledge. Abstract: In this paper, we address the task of cross-lingual semantic relatedness. We introduce a method that relies on the information extracted from Wikipedia, by exploiting the interlanguage links available between Wikipedia versions in multiple languages. Through experiments performed on several language pairs, we show that the method performs well, with a performance comparable to monolingual measures of relatedness. digital.library.unt.edu/ark:/67531/metadc31012/
- 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. digital.library.unt.edu/ark:/67531/metadc31018/
- Dynamic Agent Population in Agent-Based Distance Vector Routing
- This paper discusses dynamic agent population in agent-based distance vector routing. Abstract: The Intelligent mobile agent paradigm can be applied to a wide variety of intrinsically parallel and distributed applications. Network routing is one such application that can be mapped to an agent-based approach. The performance of any agent-based system will depend on its agent population. Although a lot of research has been conducted on agent-based systems, little consideration has been given to the importance of agent population in dynamic networks. A large number of constituent agents can increase the resource overhead of the system, thereby impeding the overall performance of the network. Hence, it is imperative to find the optimal number of agents in the system that would maximize the efficiency of the agent-based mechanism in the network. This optimal value cannot be determined manually, thereby emphasizing the need for an adaptive approach that manipulates the number of agents in the system based on its resource availability. This paper discusses an agent-based approach to Distance Vector Routing, referred as Agent-based Distance Vector Routing and also describes an adaptive approach controlling the number of agents in the network using pheromones and discusses their limitations. digital.library.unt.edu/ark:/67531/metadc132968/
- Dynamic Channel Assignment in IEEE 802.11 Networks
- This paper discusses dynamic channel assignment in IEEE 802.11 networks. Abstract: We design a dynamic channel assignment algorithm for IEEE 802.11 wireless networks. Our algorithm assigns channels dynamically in a way that minimizes channel interference generated by neighboring access points (APs) on a reference access point, resulting in higher throughput. We implement and simulate their algorithm using two versions (1: pick randomly 2: pick first) and different number of APs (4, 9, 16, and 25). Analysis of this algorithm shows an improvement by a factor of 4 (by lowering the total interference on an AP by 6 dBm on average) over default settings of having all APs use the same channel. As the number of APs is increased in a given service area, dynamic channel assignment becomes crucial; otherwise overlapping channel interference becomes a limiting factor. digital.library.unt.edu/ark:/67531/metadc30837/
- Efficient Energy Saving Scheme for On-Chip Caches
- This paper discusses efficient energy saving scheme for on-chip caches. Abstract: With the reduction in feature size the static power component, such as the leakage power, dominates the dynamic power consumption in the on-chip caches. It has been observed that all cache lines need not be kept alive at all times. Only a very few lines during a given window of time need to be actively powered from the footprint, i.e., they are accessed during that time. Earlier research has addressed the issue of how to determine the set of active lines and how long to keep them active (powered). Circuit techniques have also been developed to keep a cache line in low leakage state i.e., Drowsy State when the line is not being accessed or used. Such a cache is called drowsy cache. These circuit techniques try to achieve maximum reduction in the leakage power without losing the information content and with minimal performance penalty associated with power transitions. These techniques when used with optimal switching scheme, which decides when and what lines to drowse, results in maximum reduction in energy consumed. In this paper, the authors study the cache access pattern to evaluate them and arrive at an optimal scheme to implement the drowsy cache. The authors achieve energy reduction on the average of 88% of maximum gain achievable through the underlying circuit technique. The authors also compare the performance of their scheme with the earlier proposed schemes and show that the authors can achieve up to 6% of higher saving in cache energy for the benchmarks studied (with an average on 4% for all benchmarks with equal weights) without any additional performance penalty. digital.library.unt.edu/ark:/67531/metadc94293/
- An Efficient Non-Preemptive Real-Time Scheduling
- This paper discusses non-preemptive, real-time scheduling. Abstract: Traditional real-time systems are designed using preemptive scheduling and worst-case execution time estimates to guarantee the execution of high priority tasks. There is, however, an interest in exploring non-preemptive scheduling models for real-time systems, particularly for soft real-time multimedia applications. In this paper, we propose a new algorithm that uses multiple scheduling strategies. The goal of this research is to improve the success rate of the well-known Earliest Deadline First (EDF) approach even when the load on the system is very high. The approach, known as group-EDF (gEDF) is based on (dynamic) grouping of tasks with deadlines that are very close to each other, and using Shortest Job First (SJF) technique to schedule tasks within the group. We present results comparing gEDF and EDF using randomly generated tasks with varying execution times, release times, deadlines and tolerance to missing deadlines, under varying workloads. We believe that the grouping of tasks with similar deadlines and utilizing information other than deadlines (such as execution times, priorities or resource availability) for scheduling tasks within a group can lead to new and more efficient real-time scheduling algorithms. digital.library.unt.edu/ark:/67531/metadc30819/
- Enhanced Channel Assignment and Load Distribution in IEEE 802.11 WLANs
- This paper discusses enhanced channel assignment and load distribution in IEEE 802.11 WLANs. Abstract: An algorithm to reduce congestion and balance users' load in IEEE 802.11b/g wireless local area networks (WLANs) is presented, which takes into account overlapping channel interference between access points (APs) and the signal-to-interference ratio (SIR) experienced by the users. After finding the best channel assignment at the APs, the algorithm then finds the most congested access point (MCAP). It reexamines the users' association with APS by minimizing the congestion at the MCAP. Simulation results show that the proposed algorithm is capable of significantly reducing the overall congestion in the WLAN while mitigating channel interference. Our algorithm has also been shown to be scalable and it performs well for networks of different topologies. digital.library.unt.edu/ark:/67531/metadc30838/
- Enhancing the Undergraduate Research Experience in a Senior Design Context
- This paper discusses enhancing the undergraduate research experience in a senior design context. Abstract: This paper presents an instructional framework developed by the authors that engages senior students in a 5-credit Research and Development course incorporating project development, implementation, entrepreneurship, innovation, creativity, teamwork, and communication. The paper discusses the development and accomplishments of the course over the past four years in the context of the Quality Enhancement Plan (QEP) - an initiative at the University of Houston intended to encourage the development and enhancement of undergraduate research skills. The philosophy behind the course is to provide training and real world, small-scale project experience through the completion of a full-project lifecycle from conceptualization to prototype. Brief discussion of those projects that resulted in provisional patents, refereed journal publications, and conference presentations will be given. Some of the features of the course, such as University and industry guest speaker series and final project evaluation by the department's Industrial Advisory Board, leading professionals, faculty, technical staff and peers will be examined. The paper concludes by outlining a set of short term and long term goals for the future direction of the course. digital.library.unt.edu/ark:/67531/metadc115192/
- An Evaluation Exercise for Romanian Word Sense Disambiguation
- This paper discusses an evaluation exercise for Romanian word sense disambiguation. Abstract: This paper presents the task definition, resources, participating systems, and comparative results for a Romanian Word Sense Disambiguation task, which was organized as part of the SENSEVAL-3 evaluation exercise. Five teams with a total of seven systems were drawn to this task. digital.library.unt.edu/ark:/67531/metadc30954/
- An Evaluation Exercise for Word Alignment
- This paper discusses an evaluation exercise for word alignment. Abstract: 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 HLT/NAACL 2003 Workshop on Building and Using Parallel Texts. The shared task included Romanian-English and English-French sub-tasks, and drew the participation of seven teams from around the world. digital.library.unt.edu/ark:/67531/metadc30946/
- Exploiting Agreement and Disagreement of Human Annotators for Word Sense Disambiguation
- This paper discusses word sense disambiguation. Abstract: It is generally agreed that the success of a Word Sense Disambiguation (WSD) system depends, in large, on having enough sense annotated data available at hand, and a well-motivated sense inventory into which the disambiguations are made. The authors report a Web-based approach to (1) constructing large sense tagged corpora by exploiting agreement of Web users who contribute word sense annotation, and (2) deriving a coarse-grained sense inventory from a fine-grained inventory by exploiting disagreements of independent contributors about word senses. The authors investigate the quantity and quality of the sense tagged data collected with this approach over the past year. The authors present and evaluate an automatic clustering algorithm able to derive sense clusters that compare well with manually constructed clusters. digital.library.unt.edu/ark:/67531/metadc30948/
- Explorations in Automatic Book Summarization
- This paper discusses explorations in automatic book summarization. Abstract: Most of the text summarization research carried out to date has been concerned with the summarization of short documents (e.g., news stories, technical reports), and very little work if any has been done on the summarization of very long documents. In this paper, we try to address this gap and explore the problem of book summarization. We introduce a new data set specifically designed for the evaluation of systems for book summarization, and describe summarization techniques that explicitly account for the length of the documents. digital.library.unt.edu/ark:/67531/metadc30989/
- eXtended WordNet: progress report
- This paper discusses eXtended WordNet. Abstract: eXtended WordNet (XWN), a morphologically and semantically enhanced version of the WordNet dictionary, is currently build at SMU. There are several phases in the XWN project. This paper focuses on the semantic disambiguation stage of this project, and the preprocessing required by this stage. digital.library.unt.edu/ark:/67531/metadc83309/
- FALCON: Boosting Knowledge for Answer Engines
- This paper discusses FALCON. Abstract: This paper presents the features of FALCON, an answer engine that integrates different forms of syntactic, semantic and pragmatic knowledge for the goal of achieving better performance. The answer engine handles question reformulations, finds the expected answer type from a large hierarchy that incorporates the WordNet semantic net and extracts answers after performing unifications on the semantic forms of the question and its candidate answers. To rule out erroneous answers, it provides justification option, implemented as an abductive proof. In TREC-9, FALCON generated a score of 58% for short answers and 76% for long answers. digital.library.unt.edu/ark:/67531/metadc83296/
- Finding Semantic Associations on Express Lane
- This paper introduces a new codification scheme for efficient computation of measures in semantic networks. The scheme is particularly useful for fast computation of semantic associations between words and implementation of an informational retrieval operator for efficient search in semantic spaces. Other applications may also be possible. digital.library.unt.edu/ark:/67531/metadc30956/
- Global versus Local Call Admission Control in CDMA Cellular Networks
- This paper discusses global versus local call admission control. Abstract: We design and implement global and local CAC algorithms for CDMA networks, and compare their network throughput for various mobility scenarios. The global CAC algorithms is inherently optimized and uses global information in making every call admission decision; it yields the best possible performance but has an intensive computational complexity. The design of the local CAC algorithm uses global information but its implementation in each cell uses only local information; it only requires the number of calls currently active in that cell and thus is very simple to implement. We show that our optimized local CAC algorithm achieves almost the same performance as our global CAC algorithm for a given call arrival rate profile. digital.library.unt.edu/ark:/67531/metadc30816/
- Graph-based Ranking Algorithms for Sentence Extraction, Applied to Text Summarization
- Abstract: This paper presents an innovative unsupervised method for automatic sentence extraction using graph-based ranking algorithms. We evaluate the method in the context of a text summarization task, and show that the results obtained compare favorably with previously published results on established benchmarks. digital.library.unt.edu/ark:/67531/metadc30957/
- Grid-based Coordinated Routing in Wireless Sensor Networks
- This paper discusses grid-based coordinated routing in wireless sensor networks. Abstract: This work explores grid-based coordinated routing in wireless sensor networks and compares the energy available in the network over time for different grid sizes. A test area is divided into square-shaped grids of certain length. Fully charged battery powered nodes are randomly placed in the area with fixed source and sink nodes. One node per grid is elected as the coordinator which does the actual routing. The source node starts flooding the network with every coordinator joining in the routing. Once the flooding reaches the sink node, information is sent back to the source by finding the back route to the source. This process is continued until a node (coordinator) along that route runs out of energy. New coordinators are elected to replace the depleted ones. The source node refloods the network so that the sink can find a new back route to send information. This entire process continues until the network is partitioned and the connectivity between the source and the sink nodes is lost. We 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. digital.library.unt.edu/ark:/67531/metadc30839/
- Hybrid Energy-Aware Synchronization Algorithm in Wireless Sensor Networks
- This paper discusses hybrid energy-aware synchronization algorithm in wireless sensor networks. Abstract: We present a time synchronization scheme for wireless sensor networks that aims to conserve sensor battery power while maintaining network connectivity for as long as possible. The proposed method creates a hierarchical tree by flooding the sensor network from a designated source point. It then uses a hybrid algorithm derived from the Timing-sync Protocol for Sensor Networks (TSPN) and the Reference Broadcast Synchronization Method (RBS) to periodically synchronize sensor clocks by minimizing the number of required transmissions. In multi-hop ad-hoc networks, a depleted sensor will drop information from all other sensors that route data through it, decreasing the physical area being monitored by the network. It is therefore imperative that time synchronization schemes are aware of the number of sensors being used at any given time. The proposed method uses several techniques and thresholds to maintain network connectivity. A new source point is chosen when the current one's battery power reaches a designated energy threshold. The network is also re-flooded whenever the number of used sensors drops below another threshold. We implement and show that their scheme can provide significant power savings over both TPSN and RBS; the power reduction is even more drastic in large multi-hop sensor networks. The method also improves upon these algorithms by maintaining a large area of coverage even when some sensors lose power. digital.library.unt.edu/ark:/67531/metadc30840/
- Impact of Interference Model on Capacity in CDMA Cellular Networks
- This paper discusses an impact of interference model on capacity in CDMA cellular networks. Abstract: An overwhelming number of models in the literature use average interference for calculation of capacity of a CDMA network. In this paper, we calculate the actual per-user interference and analyze the effect of user-distribution on the capacity of a CDMA network. We show that even though the capacity obtained using average interference is a good approximation to the capacity calculated using actual interference for a uniform user distribution, the deviation can be tremendously large for non-uniform user distributions. digital.library.unt.edu/ark:/67531/metadc30817/
- Indoor Propagation Modeling at 2.4 GHZ for IEEE 802.11 Networks
- This paper discusses indoor propagation modeling. Abstract: The purpose of this study is to characterize the indoor channel for 802.11 wireless local area networks at 2.4 GHz frequency. This work presents a channel model based on measurements conducted in commonly found scenarios in buildings. These scenarios include closed corridor, open corridor, classroom, and computer lab. Path loss equations are determined using log-distance path loss model and log-normal shadowing. The Chi-square test statistic values for each access point are calculated to prove that the observed fading is a normal distribution at 5% significance level. A numerical analysis of measurements in each scenario was conducted and the study determined equations that describe path loss for each scenario. digital.library.unt.edu/ark:/67531/metadc30827/
- Instance Based Learning with Automatic Feature Selection Applied to Word Sense Disambiguation
- This paper discusses instance based learning with automatic feature selection applied to word sense disambiguation. Abstract We describe an algorithm for Word Sense Disambiguation (WSD) that relies on a lazy learner improved with automatic feature selection. The algorithm was implemented in a system that achieves excellent performance on the set of data released during the SENSEVAL-2 competition. We present the results obtained and discuss the performance of various features in the context of supervised learning algorithms for WSD. digital.library.unt.edu/ark:/67531/metadc30943/