UNT Theses and Dissertations - Browse

ABOUT BROWSE FEED

Network Security Tool for a Novice

Description: Network security is a complex field that is handled by security professionals who need certain expertise and experience to configure security systems. With the ever increasing size of the networks, managing them is going to be a daunting task. What kind of solution can be used to generate effective security configurations by both security professionals and nonprofessionals alike? In this thesis, a web tool is developed to simplify the process of configuring security systems by translating direct human language input into meaningful, working security rules. These human language inputs yield the security rules that the individual wants to implement in their network. The human language input can be as simple as, "Block Facebook to my son's PC". This tool will translate these inputs into specific security rules and install the translated rules into security equipment such as virtualized Cisco FWSM network firewall, Netfilter host-based firewall, and Snort Network Intrusion Detection. This tool is implemented and tested in both a traditional network and a cloud environment. One thousand input policies were collected from various users such as staff from UNT departments' and health science, including individuals with network security background as well as students with a non-computer science background to analyze the tool's performance. The tool is tested for its accuracy (91%) in generating a security rule. It is also tested for accuracy of the translated rule (86%) compared to a standard rule written by security professionals. Nevertheless, the network security tool built has shown promise to both experienced and inexperienced people in network security field by simplifying the provisioning process to result in accurate and effective network security rules.
Access: This item is restricted to UNT Community Members. Login required if off-campus.
Date: August 2016
Creator: Ganduri, Rajasekhar
Partner: UNT Libraries

An Empirical Study of How Novice Programmers Use the Web

Description: Students often use the web as a source of help for problems that they encounter on programming assignments.In this work, we seek to understand how students use the web to search for help on their assignments.We used a mixed methods approach with 344 students who complete a survey and 41 students who participate in a focus group meetings and helped in recording data about their search habits.The survey reveals data about student reported search habits while the focus group uses a web browser plug-in to record actual search patterns.We examine the results collectively and as broken down by class year.Survey results show that at least 2/3 of the students from each class year rely on search engines to locate resources for help with their programming bugs in at least half of their assignments;search habits vary by class year;and the value of different types of resources such as tutorials and forums varies by class year.Focus group results exposes the high frequency web sites used by the students in solving their programming assignments.
Date: May 2016
Creator: Tula, Naveen
Partner: UNT Libraries

Learning from small data set for object recognition in mobile platforms.

Description: Did you stand at a door with a bunch of keys and tried to find the right one to unlock the door? Did you hold a flower and wonder the name of it? A need of object recognition could rise anytime and any where in our daily lives. With the development of mobile devices object recognition applications become possible to provide immediate assistance. However, performing complex tasks in even the most advanced mobile platforms still faces great challenges due to the limited computing resources and computing power. In this thesis, we present an object recognition system that resides and executes within a mobile device, which can efficiently extract image features and perform learning and classification. To account for the computing constraint, a novel feature extraction method that minimizes the data size and maintains data consistency is proposed. This system leverages principal component analysis method and is able to update the trained classifier when new examples become available . Our system relieves users from creating a lot of examples and makes it user friendly. The experimental results demonstrate that a learning method trained with a very small number of examples can achieve recognition accuracy above 90% in various acquisition conditions. In addition, the system is able to perform learning efficiently.
Access: This item is restricted to UNT Community Members. Login required if off-campus.
Date: May 2016
Creator: Liu, Siyuan
Partner: UNT Libraries

Algorithm Optimizations in Genomic Analysis Using Entropic Dissection

Description: In recent years, the collection of genomic data has skyrocketed and databases of genomic data are growing at a faster rate than ever before. Although many computational methods have been developed to interpret these data, they tend to struggle to process the ever increasing file sizes that are being produced and fail to take advantage of the advances in multi-core processors by using parallel processing. In some instances, loss of accuracy has been a necessary trade off to allow faster computation of the data. This thesis discusses one such algorithm that has been developed and how changes were made to allow larger input file sizes and reduce the time required to achieve a result without sacrificing accuracy. An information entropy based algorithm was used as a basis to demonstrate these techniques. The algorithm dissects the distinctive patterns underlying genomic data efficiently requiring no a priori knowledge, and thus is applicable in a variety of biological research applications. This research describes how parallel processing and object-oriented programming techniques were used to process larger files in less time and achieve a more accurate result from the algorithm. Through object oriented techniques, the maximum allowable input file size was significantly increased from 200 mb to 2000 mb. Using parallel processing techniques allowed the program to finish processing data in less than half the time of the sequential version. The accuracy of the algorithm was improved by reducing data loss throughout the algorithm. Finally, adding user-friendly options enabled the program to use requests more effectively and further customize the logic used within the algorithm.
Date: August 2015
Creator: Danks, Jacob R.
Partner: UNT Libraries

Automatic Removal of Complex Shadows From Indoor Videos

Description: Shadows in indoor scenarios are usually characterized with multiple light sources that produce complex shadow patterns of a single object. Without removing shadow, the foreground object tends to be erroneously segmented. The inconsistent hue and intensity of shadows make automatic removal a challenging task. In this thesis, a dynamic thresholding and transfer learning-based method for removing shadows is proposed. The method suppresses light shadows with a dynamically computed threshold and removes dark shadows using an online learning strategy that is built upon a base classifier trained with manually annotated examples and refined with the automatically identified examples in the new videos. Experimental results demonstrate that despite variation of lighting conditions in videos our proposed method is able to adapt to the videos and remove shadows effectively. The sensitivity of shadow detection changes slightly with different confidence levels used in example selection for classifier retraining and high confidence level usually yields better performance with less retraining iterations.
Date: August 2015
Creator: Mohapatra, Deepankar
Partner: UNT Libraries

An Empirical Study of Software Debugging Games with Introductory Students

Description: Bug Fixer is a web-based application that complements lectures with hands-on exercises that encourage students to think about the logic in programs. Bug Fixer presents students with code that has several bugs that they must fix. The process of fixing the bugs forces students to conceptually think about the code and reinforces their understanding of the logic behind algorithms. In this work, we conducted a study using Bug Fixer with undergraduate students in the CSCE1040 course at University of North Texas to evaluate whether the system increases their conceptual understanding of the algorithms and improves their Software Testing skills. Students participated in weekly activities to fix bugs in code. Most students enjoyed Bug Fixer and recommend the system for future use. Students typically reported a better understanding of the algorithms used in class. We observed a slight increase of passing grades for students who participated in our study compared to students in other sections of the course with the same instructor who did not participate in our study. The students who did not report a positive experience provide comments for future improvements that we plan to address in future work.
Date: August 2015
Creator: Reynolds, Lisa Marie
Partner: UNT Libraries

Freeform Cursive Handwriting Recognition Using a Clustered Neural Network

Description: Optical character recognition (OCR) software has advanced greatly in recent years. Machine-printed text can be scanned and converted to searchable text with word accuracy rates around 98%. Reasonably neat hand-printed text can be recognized with about 85% word accuracy. However, cursive handwriting still remains a challenge, with state-of-the-art performance still around 75%. Algorithms based on hidden Markov models have been only moderately successful, while recurrent neural networks have delivered the best results to date. This thesis explored the feasibility of using a special type of feedforward neural network to convert freeform cursive handwriting to searchable text. The hidden nodes in this network were grouped into clusters, with each cluster being trained to recognize a unique character bigram. The network was trained on writing samples that were pre-segmented and annotated. Post-processing was facilitated in part by using the network to identify overlapping bigrams that were then linked together to form words and sentences. With dictionary assisted post-processing, the network achieved word accuracy of 66.5% on a small, proprietary corpus. The contributions in this thesis are threefold: 1) the novel clustered architecture of the feed-forward neural network, 2) the development of an expanded set of observers combining image masks, modifiers, and feature characterizations, and 3) the use of overlapping bigrams as the textual working unit to assist in context analysis and reconstruction.
Date: August 2015
Creator: Bristow, Kelly H.
Partner: UNT Libraries

Integrity Verification of Applications on Radium Architecture

Description: Trusted Computing capability has become ubiquitous these days, and it is being widely deployed into consumer devices as well as enterprise platforms. As the number of threats is increasing at an exponential rate, it is becoming a daunting task to secure the systems against them. In this context, the software integrity measurement at runtime with the support of trusted platforms can be a better security strategy. Trusted Computing devices like TPM secure the evidence of a breach or an attack. These devices remain tamper proof if the hardware platform is physically secured. This type of trusted security is crucial for forensic analysis in the aftermath of a breach. The advantages of trusted platforms can be further leveraged if they can be used wisely. RADIUM (Race-free on-demand Integrity Measurement Architecture) is one such architecture, which is built on the strength of TPM. RADIUM provides an asynchronous root of trust to overcome the TOC condition of DRTM. Even though the underlying architecture is trusted, attacks can still compromise applications during runtime by exploiting their vulnerabilities. I propose an application-level integrity measurement solution that fits into RADIUM, to expand the trusted computing capability to the application layer. This is based on the concept of program invariants that can be used to learn the correct behavior of an application. I used Daikon, a tool to obtain dynamic likely invariants, and developed a method of observing these properties at runtime to verify the integrity. The integrity measurement component was implemented as a Python module on top of Volatility, a virtual machine introspection tool. My approach is a first step towards integrity attestation, using hypervisor-based introspection on RADIUM and a proof of concept of application-level measurement capability.
Date: August 2015
Creator: Tarigopula, Mohan Krishna
Partner: UNT Libraries

Maintaining Web Applications Integrity Running on Radium

Description: Computer security attacks take place due to the presence of vulnerabilities and bugs in software applications. Bugs and vulnerabilities are the result of weak software architecture and lack of standard software development practices. Despite the fact that software companies are investing millions of dollars in the research and development of software designs security risks are still at large. In some cases software applications are found to carry vulnerabilities for many years before being identified. A recent such example is the popular Heart Bleed Bug in the Open SSL/TSL. In today’s world, where new software application are continuously being developed for a varied community of users; it’s highly unlikely to have software applications running without flaws. Attackers on computer system securities exploit these vulnerabilities and bugs and cause threat to privacy without leaving any trace. The most critical vulnerabilities are those which are related to the integrity of the software applications. Because integrity is directly linked to the credibility of software application and data it contains. Here I am giving solution of maintaining web applications integrity running on RADIUM by using daikon. Daikon generates invariants, these invariants are used to maintain the integrity of the web application and also check the correct behavior of web application at run time on RADIUM architecture in case of any attack or malware. I used data invariants and program flow invariants in my solution to maintain the integrity of web-application against such attack or malware. I check the behavior of my proposed invariants at run-time using Lib-VMI/Volatility memory introspection tool. This is a novel approach and proof of concept toward maintaining web application integrity on RADIUM.
Date: August 2015
Creator: Ur-Rehman, Wasi
Partner: UNT Libraries

Radium: Secure Policy Engine in Hypervisor

Description: The basis of today’s security systems is the trust and confidence that the system will behave as expected and are in a known good trusted state. The trust is built from hardware and software elements that generates a chain of trust that originates from a trusted known entity. Leveraging hardware, software and a mandatory access control policy technology is needed to create a trusted measurement environment. Employing a control layer (hypervisor or microkernel) with the ability to enforce a fine grained access control policy with hyper call granularity across multiple guest virtual domains can ensure that any malicious environment to be contained. In my research, I propose the use of radium's Asynchronous Root of Trust Measurement (ARTM) capability incorporated with a secure mandatory access control policy engine that would mitigate the limitations of the current hardware TPM solutions. By employing ARTM we can leverage asynchronous use of boot, launch, and use with the hypervisor proving its state and the integrity of the secure policy. My solution is using Radium (Race free on demand integrity architecture) architecture that will allow a more detailed measurement of applications at run time with greater semantic knowledge of the measured environments. Radium incorporation of a secure access control policy engine will give it the ability to limit or empower a virtual domain system. It can also enable the creation of a service oriented model of guest virtual domains that have the ability to perform certain operations such as introspecting other virtual domain systems to determine the integrity or system state and report it to a remote entity.
Date: August 2015
Creator: Shah, Tawfiq M
Partner: UNT Libraries

Towards Resistance Detection in Health Behavior Change Dialogue Systems

Description: One of the challenges fairly common in motivational interviewing is patient resistance to health behavior change. Hence, automated dialog systems aimed at counseling patients need to be capable of detecting resistance and appropriately altering dialog. This thesis focusses primarily on the development of such a system for automatic identification of patient resistance to behavioral change. This enables the dialogue system to direct the discourse towards a more agreeable ground and helping the patient overcome the obstacles in his or her way to change. This thesis also proposes a dialogue system framework for health behavior change via natural language analysis and generation. The proposed framework facilitates automated motivational interviewing from clinical psychology and involves three broad stages: rapport building and health topic identification, assessment of the patient’s opinion about making a change, and developing a plan. Using this framework patients can be encouraged to reflect on the options available and choose the best for a healthier life.
Date: August 2015
Creator: Sarma, Bandita
Partner: UNT Libraries

Unique Channel Email System

Description: Email connects 85% of the world. This paper explores the pattern of information overload encountered by majority of email users and examine what steps key email providers are taking to combat the problem. Besides fighting spam, popular email providers offer very limited tools to reduce the amount of unwanted incoming email. Rather, there has been a trend to expand storage space and aid the organization of email. Storing email is very costly and harmful to the environment. Additionally, information overload can be detrimental to productivity. We propose a simple solution that results in drastic reduction of unwanted mail, also known as graymail.
Date: August 2015
Creator: Balakchiev, Milko
Partner: UNT Libraries

Classifying Pairwise Object Interactions: A Trajectory Analytics Approach

Description: We have a huge amount of video data from extensively available surveillance cameras and increasingly growing technology to record the motion of a moving object in the form of trajectory data. With proliferation of location-enabled devices and ongoing growth in smartphone penetration as well as advancements in exploiting image processing techniques, tracking moving objects is more flawlessly achievable. In this work, we explore some domain-independent qualitative and quantitative features in raw trajectory (spatio-temporal) data in videos captured by a fixed single wide-angle view camera sensor in outdoor areas. We study the efficacy of those features in classifying four basic high level actions by employing two supervised learning algorithms and show how each of the features affect the learning algorithms’ overall accuracy as a single factor or confounded with others.
Date: May 2015
Creator: Janmohammadi, Siamak
Partner: UNT Libraries

Distributed Frameworks Towards Building an Open Data Architecture

Description: Data is everywhere. The current Technological advancements in Digital, Social media and the ease at which the availability of different application services to interact with variety of systems are causing to generate tremendous volumes of data. Due to such varied services, Data format is now not restricted to only structure type like text but can generate unstructured content like social media data, videos and images etc. The generated Data is of no use unless been stored and analyzed to derive some Value. Traditional Database systems comes with limitations on the type of data format schema, access rates and storage sizes etc. Hadoop is an Apache open source distributed framework that support storing huge datasets of different formatted data reliably on its file system named Hadoop File System (HDFS) and to process the data stored on HDFS using MapReduce programming model. This thesis study is about building a Data Architecture using Hadoop and its related open source distributed frameworks to support a Data flow pipeline on a low commodity hardware. The Data flow components are, sourcing data, storage management on HDFS and data access layer. This study also discuss about a use case to utilize the architecture components. Sqoop, a framework to ingest the structured data from database onto Hadoop and Flume is used to ingest the semi-structured Twitter streaming json data on to HDFS for analysis. The data sourced using Sqoop and Flume have been analyzed using Hive for SQL like analytics and at a higher level of data access layer, Hadoop has been compared with an in memory computing system using Spark. Significant differences in query execution performances have been analyzed when working with Hadoop and Spark frameworks. This integration helps for ingesting huge Volumes of streaming json Variety data to derive better Value based analytics using Hive and ...
Date: May 2015
Creator: Venumuddala, Ramu Reddy
Partner: UNT Libraries

Smartphone-based Household Travel Survey - a Literature Review, an App, and a Pilot Survey

Description: High precision data from household travel survey (HTS) is extremely important for the transportation research, traffic models and policy formulation. Traditional methods of data collection were imprecise because they relied on people’s memories of trip information, such as date and location, and the remainder data had to be obtained by certain supplemental tools. The traditional methods suffered from intensive labor, large time consumption, and unsatisfactory data precision. Recent research trends to employ smartphone apps to collect HTS data. In this study, there are two goals to be addressed. First, a smartphone app is developed to realize a smartphone-based method only for data collection. Second, the researcher evaluates whether this method can supply or replace the traditional tools of HTS. Based on this premise, the smartphone app, TravelSurvey, is specially developed and used for this study. TravelSurvey is currently compatible with iPhone 4 or higher and iPhone Operating System (iOS) 6 or higher, except iPhone 6 or iPhone 6 plus and iOS 8. To evaluate the feasibility, eight individuals are recruited to participate in a pilot HTS. Afterwards, seven of them are involved in a semi-structured interview. The interview is designed to collect interviewees’ feedback directly, so the interview mainly concerns the users’ experience of TravelSurvey. Generally, the feedback is positive. In this study, the pilot HTS data is successfully uploaded to the server by the participants, and the interviewees prefer this smartphone-based method. Therefore, as a new tool, the smartphone-based method feasibly supports a typical HTS for data collection.
Access: This item is restricted to UNT Community Members. Login required if off-campus.
Date: December 2014
Creator: Wang, Qian
Partner: UNT Libraries

General Purpose Computing in Gpu - a Watermarking Case Study

Description: The purpose of this project is to explore the GPU for general purpose computing. The GPU is a massively parallel computing device that has a high-throughput, exhibits high arithmetic intensity, has a large market presence, and with the increasing computation power being added to it each year through innovations, the GPU is a perfect candidate to complement the CPU in performing computations. The GPU follows the single instruction multiple data (SIMD) model for applying operations on its data. This model allows the GPU to be very useful for assisting the CPU in performing computations on data that is highly parallel in nature. The compute unified device architecture (CUDA) is a parallel computing and programming platform for NVIDIA GPUs. The main focus of this project is to show the power, speed, and performance of a CUDA-enabled GPU for digital video watermark insertion in the H.264 video compression domain. Digital video watermarking in general is a highly computationally intensive process that is strongly dependent on the video compression format in place. The H.264/MPEG-4 AVC video compression format has high compression efficiency at the expense of having high computational complexity and leaving little room for an imperceptible watermark to be inserted. Employing a human visual model to limit distortion and degradation of visual quality introduced by the watermark is a good choice for designing a video watermarking algorithm though this does introduce more computational complexity to the algorithm. Research is being conducted into how the CPU-GPU execution of the digital watermark application can boost the speed of the applications several times compared to running the application on a standalone CPU using NVIDIA visual profiler to optimize the application.
Date: August 2014
Creator: Hanson, Anthony
Partner: UNT Libraries

Ddos Defense Against Botnets in the Mobile Cloud

Description: Mobile phone advancements and ubiquitous internet connectivity are resulting in ever expanding possibilities in the application of smart phones. Users of mobile phones are now capable of hosting server applications from their personal devices. Whether providing services individually or in an ad hoc network setting the devices are currently not configured for defending against distributed denial of service (DDoS) attacks. These attacks, often launched from a botnet, have existed in the space of personal computing for decades but recently have begun showing up on mobile devices. Research is done first into the required steps to develop a potential botnet on the Android platform. This includes testing for the amount of malicious traffic an Android phone would be capable of generating for a DDoS attack. On the other end of the spectrum is the need of mobile devices running networked applications to develop security against DDoS attacks. For this mobile, phones are setup, with web servers running Apache to simulate users running internet connected applications for either local ad hoc networks or serving to the internet. Testing is done for the viability of using commonly available modules developed for Apache and intended for servers as well as finding baseline capabilities of mobiles to handle higher traffic volumes. Given the unique challenge of the limited resources a mobile phone can dedicate to Apache when compared to a dedicated hosting server a new method was needed. A proposed defense algorithm is developed for mitigating DDoS attacks against the mobile server that takes into account the limited resources available on the mobile device. The algorithm is tested against TCP socket flooding for effectiveness and shown to perform better than the common Apache module installations on a mobile device.
Date: May 2014
Creator: Jensen, David
Partner: UNT Libraries

3GPP Long Term Evolution LTE Scheduling

Description: Future generation cellular networks are expected to deliver an omnipresent broadband access network for an endlessly increasing number of subscribers. Long term Evolution (LTE) represents a significant milestone towards wireless networks known as 4G cellular networks. A key feature of LTE is the implementation of enhanced Radio Resource Management (RRM) mechanism to improve the system performance. The structure of LTE networks was simplified by diminishing the number of the nodes of the core network. Also, the design of the radio protocol architecture is quite unique. In order to achieve high data rate in LTE, 3rd Generation Partnership Project (3GPP) has selected Orthogonal Frequency Division Multiplexing (OFDM) as an appropriate scheme in terms of downlinks. However, the proper scheme for an uplink is the Single-Carrier Frequency Domain Multiple Access due to the peak-to-average-power-ratio (PAPR) constraint. LTE packet scheduling plays a primary role as part of RRM to improve the system’s data rate as well as supporting various QoS requirements of mobile services. The major function of the LTE packet scheduler is to assign Physical Resource Blocks (PRBs) to mobile User Equipment (UE). In our work, we formed a proposed packet scheduler algorithm. The proposed scheduler algorithm acts based on the number of UEs attached to the eNodeB. To evaluate the proposed scheduler algorithm, we assumed two different scenarios based on a number of UEs. When the number of UE is lower than the number of PRBs, the UEs with highest Channel Quality Indicator (CQI) will be assigned PRBs. Otherwise, the scheduler will assign PRBs based on a given proportional fairness metric. The eNodeB’s throughput is increased when the proposed algorithm was implemented.
Date: December 2013
Creator: Alotaibi, Sultan
Partner: UNT Libraries

Design and Analysis of Novel Verifiable Voting Schemes

Description: Free and fair elections are the basis for democracy, but conducting elections is not an easy task. Different groups of people are trying to influence the outcome of the election in their favor using the range of methods, from campaigning for a particular candidate to well-financed lobbying. Often the stakes are too high, and the methods are illegal. Two main properties of any voting scheme are the privacy of a voter’s choice and the integrity of the tally. Unfortunately, they are mutually exclusive. Integrity requires making elections transparent and auditable, but at the same time, we must preserve a voter’s privacy. It is always a trade-off between these two requirements. Current voting schemes favor privacy over auditability, and thus, they are vulnerable to voting fraud. I propose two novel voting systems that can achieve both privacy and verifiability. The first protocol is based on cryptographical primitives to ensure the integrity of the final tally and privacy of the voter. The second protocol is a simple paper-based voting scheme that achieves almost the same level of security without usage of cryptography.
Date: December 2013
Creator: Yestekov, Yernat
Partner: UNT Libraries

Qos Aware Service Oriented Architecture

Description: Service-oriented architecture enables web services to operate in a loosely-coupled setting and provides an environment for dynamic discovery and use of services over a network using standards such as WSDL, SOAP, and UDDI. Web service has both functional and non-functional characteristics. This thesis work proposes to add QoS descriptions (non-functional properties) to WSDL and compose various services to form a business process. This composition of web services also considers QoS properties along with functional properties and the composed services can again be published as a new Web Service and can be part of any other composition using Composed WSDL.
Date: August 2013
Creator: Adepu, Sagarika
Partner: UNT Libraries

Modeling Alcohol Consumption Using Blog Data

Description: How do the content and writing style of people who drink alcohol beverages stand out from non-drinkers? How much information can we learn about a person's alcohol consumption behavior by reading text that they have authored? This thesis attempts to extend the methods deployed in authorship attribution and authorship profiling research into the domain of automatically identifying the human action of drinking alcohol beverages. I examine how a psycholinguistics dictionary (the Linguistics Inquiry and Word Count lexicon, developed by James Pennebaker), together with Kenneth Burke's concept of words as symbols of human action, and James Wertsch's concept of mediated action provide a framework for analyzing meaningful data patterns from the content of blogs written by consumers of alcohol beverages. The contributions of this thesis to the research field are twofold. First, I show that it is possible to automatically identify blog posts that have content related to the consumption of alcohol beverages. And second, I provide a framework and tools to model human behavior through text analysis of blog data.
Date: May 2013
Creator: Koh, Kok Chuan
Partner: UNT Libraries

Automated Classification of Emotions Using Song Lyrics

Description: This thesis explores the classification of emotions in song lyrics, using automatic approaches applied to a novel corpus of 100 popular songs. I use crowd sourcing via Amazon Mechanical Turk to collect line-level emotions annotations for this collection of song lyrics. I then build classifiers that rely on textual features to automatically identify the presence of one or more of the following six Ekman emotions: anger, disgust, fear, joy, sadness and surprise. I compare different classification systems and evaluate the performance of the automatic systems against the manual annotations. I also introduce a system that uses data collected from the social network Twitter. I use the Twitter API to collect a large corpus of tweets manually labeled by their authors for one of the six emotions of interest. I then compare the classification of emotions obtained when training on data automatically collected from Twitter versus data obtained through crowd sourced annotations.
Date: December 2012
Creator: Schellenberg, Rajitha
Partner: UNT Libraries

Automatic Tagging of Communication Data

Description: Globally distributed software teams are widespread throughout industry. But finding reliable methods that can properly assess a team's activities is a real challenge. Methods such as surveys and manual coding of activities are too time consuming and are often unreliable. Recent advances in information retrieval and linguistics, however, suggest that automated and/or semi-automated text classification algorithms could be an effective way of finding differences in the communication patterns among individuals and groups. Communication among group members is frequent and generates a significant amount of data. Thus having a web-based tool that can automatically analyze the communication patterns among global software teams could lead to a better understanding of group performance. The goal of this thesis, therefore, is to compare automatic and semi-automatic measures of communication and evaluate their effectiveness in classifying different types of group activities that occur within a global software development project. In order to achieve this goal, we developed a web-based component that can be used to help clean and classify communication activities. The component was then used to compare different automated text classification techniques on various group activities to determine their effectiveness in correctly classifying data from a global software development team project.
Date: August 2012
Creator: Hoyt, Matthew Ray
Partner: UNT Libraries

A Smooth-turn Mobility Model for Airborne Networks

Description: In this article, I introduce a novel airborne network mobility model, called the Smooth Turn Mobility Model, that captures the correlation of acceleration for airborne vehicles across time and spatial coordinates. E?ective routing in airborne networks (ANs) relies on suitable mobility models that capture the random movement pattern of airborne vehicles. As airborne vehicles cannot make sharp turns as easily as ground vehicles do, the widely used mobility models for Mobile Ad Hoc Networks such as Random Waypoint and Random Direction models fail. Our model is realistic in capturing the tendency of airborne vehicles toward making straight trajectory and smooth turns with large radius, and whereas is simple enough for tractable connectivity analysis and routing design.
Date: August 2012
Creator: He, Dayin
Partner: UNT Libraries