Researchers have been involved for decades in search of an efficient skin detection method. Yet current methods have not overcome the major limitations. To overcome these limitations, in this dissertation, a clustering and region growing based skin detection method is proposed. These methods together with a significant insight result in a more effective algorithm. The insight concerns a capability to define dynamically the number of clusters in a collection of pixels organized as an image. In clustering for most problem domains, the number of clusters is fixed a priori and does not perform effectively over a wide variety of data contents. Therefore, in this dissertation, a skin detection method has been proposed using the above findings and validated. This method assigns the number of clusters based on image properties and ultimately allows freedom from manual thresholding or other manual operations. The dynamic determination of clustering outcomes allows for greater automation of skin detection when dealing with uncertain real-world conditions.

Worms have caused significant destruction over the last few years. Network security elements such as firewalls, IDS, etc have been ineffective against worms. Some worms are so fast that a manual intervention is not possible. This brings in the need for a stronger security architecture which can automatically react to stop worm propagation. The method has to be signature independent so that it can stop new worms. In this thesis, an automated defense system (ADS) is developed to automate defense against worms and contain the worm to a level where manual intervention is possible. This is accomplished with a two level architecture with feedback at each level. The inner loop is based on control system theory and uses the properties of PID (proportional, integral and differential controller). The outer loop works at the network level and stops the worm to reach its spread saturation point. In our lab setup, we verified that with only inner loop active the worm was delayed, and with both loops active we were able to restrict the propagation to 10% of the targeted hosts. One concern for deployment of a worm containment mechanism was degradation of throughput for legitimate traffic. We found that with proper …

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.

Due to the long duration required to perform manual knowledge entry by human knowledge engineers it is desirable to find methods to automatically acquire knowledge about the world by accessing online information. In this work I examine using the Cyc ontology to guide the creation of Naïve Bayes classifiers to provide knowledge about items described in Wikipedia articles. Given an initial set of Wikipedia articles the system uses the ontology to create positive and negative training sets for the classifiers in each category. The order in which classifiers are generated and used to test articles is also guided by the ontology. The research conducted shows that a system can be created that utilizes statistical text classification methods to extract information from an ad-hoc generated information source like Wikipedia for use in a formal semantic ontology like Cyc. Benefits and limitations of the system are discussed along with future work.

Negation is a complex phenomenon present in all human languages, allowing for the uniquely human capacities of denial, contradiction, misrepresentation, lying, and irony. It is in the first place a phenomenon of semantical opposition. Sentences containing negation are generally (a) less informative than affirmative ones, (b) morphosyntactically more marked—all languages have negative markers while only a few have affirmative markers, and (c) psychologically more complex and harder to process. Negation often conveys positive meaning. This meaning ranges from implicatures to entailments. In this dissertation, I develop a system to reveal the underlying positive interpretation of negation. I first identify which words are intended to be negated (i.e, the focus of negation) and second, I rewrite those tokens to generate an actual positive interpretation. I identify the focus of negation by scoring probable foci along a continuous scale. One of the obstacles to exploring foci scoring is that no public datasets exist for this task. Thus, to study this problem I create new corpora. The corpora contain verbal, nominal and adjectival negations and their potential positive interpretations along with their scores ranging from 1 to 5. Then, I use supervised learning models for scoring the focus of negation. In order to …

Socrates articulated it best, "Speak, so I may see you." Indeed, language represents an invisible probe into the mind. It is the medium through which we express our deepest thoughts, our aspirations, our views, our feelings, our inner reality. From the beginning of artificial intelligence, researchers have sought to impart human like understanding to machines. As much of our language represents a form of self expression, capturing thoughts, beliefs, evaluations, opinions, and emotions which are not available for scrutiny by an outside observer, in the field of natural language, research involving these aspects has crystallized under the name of subjectivity and sentiment analysis. While subjectivity classification labels text as either subjective or objective, sentiment classification further divides subjective text into either positive, negative or neutral. In this thesis, I investigate techniques of generating tools and resources for subjectivity analysis that do not rely on an existing natural language processing infrastructure in a given language. This constraint is motivated by the fact that the vast majority of human languages are scarce from an electronic point of view: they lack basic tools such as part-of-speech taggers, parsers, or basic resources such as electronic text, annotated corpora or lexica. This severely limits the …

Emergency Medical Dispatch Protocols are guidelines that a 9-1-1 dispatcher uses to evaluate the nature of emergency, resources to send and the nature of help provided to the 9-1-1 caller. The current Dispatch Protocols are based on voice only call. But the Next Generation 9-1-1 (NG9-1-1) architecture will allow multimedia emergency calls. In this thesis I analyze and model the Emergency Medical Dispatch Protocols for NG9-1-1 architecture. I have identified various technical aspects to improve the NG9-1-1 Dispatch Protocols. The devices (smartphone) at the caller end have advanced to a point where they can be used to send and receive video, pictures and text. There are sensors embedded in them that can be used for initial diagnosis of the injured person. There is a need to improve the human computer (smartphone) interface to take advantage of technology so that callers can easily make use of various features available to them. The dispatchers at the 9-1-1 call center can make use of these new protocols to improve the quality and the response time. They will have capability of multiple media streams to interact with the caller and the first responders.The specific contributions in this thesis include developing applications that use smartphone …

This work presents an investigation of a piezoelectric sensor based energy harvesting system, which collects energy from the surrounding environment. Increasing costs and scarcity of fossil fuels is a great concern today for supplying power to electronic devices. Furthermore, generating electricity by ordinary methods is a complicated process. Disposal of chemical batteries and cables is polluting the nature every day. Due to these reasons, research on energy harvesting from renewable resources has become mandatory in order to achieve improved methods and strategies of generating and storing electricity. Many low power devices being used in everyday life can be powered by harvesting energy from natural energy resources. Power overhead and power energy efficiency is of prime concern in electronic circuits. In this work, an energy harvester is modeled and simulated in Simscape™ for the functional analysis and comparison of achieved outcomes with previous work. Results demonstrate that the harvester produces power in the 0 μW to 100 μW range, which is an adequate amount to provide supply to low power devices. Power efficiency calculations also demonstrate that the implemented harvester is capable of generating and storing power for low power pervasive applications.

Colonoscopy is an endoscopic technique that allows a physician to inspect the mucosa of the human colon. Previous methods and software solutions to detect informative frames in a colonoscopy video (a process called informative frame filtering or IFF) have been hugely ineffective in (1) covering the proper definition of an informative frame in the broadest sense and (2) striking an optimal balance between accuracy and speed of classification in both real-time and non real-time medical procedures. In my thesis, I propose a more effective method and faster software solutions for IFF which is more effective due to the introduction of a heuristic algorithm (derived from experimental analysis of typical colon features) for classification. It contributed to a 5-10% boost in various performance metrics for IFF. The software modules are faster due to the incorporation of sophisticated parallel-processing oriented coding techniques on modern microprocessors. Two IFF modules were created, one for post-procedure and the other for real-time. Code optimizations through NVIDIA CUDA for GPU processing and/or CPU multi-threading concepts embedded in two significant microprocessor design philosophies (multi-core design and many-core design) resulted a 5-fold acceleration for the post-procedure module and a 40-fold acceleration for the real-time module. Some innovative software modules, …

I start with a brief introduction to the graphics processing unit (GPU) as well as general-purpose computation on modern graphics hardware (GPGPU). Next, I explore the motivations for GPGPU programming, and the capabilities of modern GPUs (including advantages and disadvantages). Also, I give the background required for further exploring GPU programming, including the terminology used and the resources available. Finally, I include a comprehensive survey of previous and current GPGPU work, and end with a look at the future of GPU programming.

The goal of this thesis is to support the automated identification of accessibility in user reviews or bug reports, to help technology professionals prioritize their handling, and, thus, to create more inclusive apps. Particularly, we propose a model that takes as input accessibility user reviews or bug reports and learns their keyword-based features to make a classification decision, for a given review, on whether it is about accessibility or not. Our empirically driven study follows a mixture of qualitative and quantitative methods. We introduced models that can accurately identify accessibility reviews and bug reports and automate detecting them. Our models can automatically classify app reviews and bug reports as accessibility-related or not so developers can easily detect accessibility issues with their products and improve them to more accessible and inclusive apps utilizing the users' input. Our goal is to create a sustainable change by including a model in the developer's software maintenance pipeline and raising awareness of existing errors that hinder the accessibility of mobile apps, which is a pressing need. In light of our findings from the Blackboard case study, Blackboard and the course material are not easily accessible to deaf students and hard of hearing. Thus, deaf students …

While pragmatics, through its integration of situational awareness and real world relevant knowledge, offers a high level of analysis that is suitable for real interpretation of natural dialogue, semantics, on the other end, represents a lower yet more tractable and affordable linguistic level of analysis using current technologies. Generally, the understanding of semantic meaning in literature has revolved around the famous quote ``You shall know a word by the company it keeps''. In this thesis we investigate the role of context constituents in decoding the semantic meaning of the engulfing context; specifically we probe the role of salient concepts, defined as content-bearing expressions which afford encyclopedic definitions, as a suitable source of semantic clues to an unambiguous interpretation of context. Furthermore, we integrate this world knowledge in building a new and robust unsupervised semantic model and apply it to entail semantic relatedness between textual pairs, whether they are words, sentences or paragraphs. Moreover, we explore the abstraction of semantics across languages and utilize our findings into building a novel multi-lingual semantic relatedness model exploiting information acquired from various languages. We demonstrate the effectiveness and the superiority of our mono-lingual and multi-lingual models through a comprehensive set of evaluations on specialized …

The proposed tool will provide users with a platform to access a side-by-side comparison of classroom assessment and job posting requirements. Using techniques and methodologies from NLP, machine learning, data analysis, and data mining: the employed algorithm analyzes job postings and classroom assessments, extracts and classifies skill units within, then compares sets of skills from different input volumes. This effectively provides a predicted alignment between academic and career sources, both federal and industrial. The compilation of tool results indicates an overall accuracy score of 82%, and an alignment score of only 75.5% between the input assessments and overall job postings. These results describe that the 50 UNT assessments and 5,000 industry and federal job postings examined, demonstrate a compatibility (alignment) of 75.5%; and, that this measure was calculated using a tool operating at an 82% precision rate.

Consumers demand new and innovative personal computing devices every 2 years when their cellular phone service contracts are renewed. Yet, a 2 year development cycle for the concurrent development of both hardware and software is nearly impossible. As more components and features are added to the devices, maintaining this 2 year cycle with current tools will become commensurately harder. This dissertation delves into the feasibility of simplifying the development of such systems by employing heterogeneous systems on a chip in conjunction with a retargetable compiler such as the hybrid computer retargetable compiler (Hy-C). An example of a simple architecture description of sufficient detail for use with a retargetable compiler like Hy-C is provided. As a software engineer with 30 years of experience, I have witnessed numerous system failures. A plethora of software development paradigms and tools have been employed to prevent software errors, but none have been completely successful. Much discussion centers on software development in the military contracting market, as that is my background. The dissertation reviews those tools, as well as some existing retargetable compilers, in an attempt to determine how those errors occurred and how a system like Hy-C could assist in reducing future software errors. In …

Modern consumer electronic systems are mostly based on analog and digital circuits and are designed as analog/mixed-signal systems on chip (AMS-SoCs). the integration of analog and digital circuits on the same die makes the system cost effective. in AMS-SoCs, analog and mixed-signal portions have not traditionally received much attention due to their complexity. As the fabrication technology advances, the simulation times for AMS-SoC circuits become more complex and take significant amounts of time. the time allocated for the circuit design and optimization creates a need to reduce the simulation time. the time constraints placed on designers are imposed by the ever-shortening time to market and non-recurrent cost of the chip. This dissertation proposes the use of a novel method, called metamodeling, and intelligent optimization algorithms to reduce the design time. Metamodel-based ultra-fast design flows are proposed and investigated. Metamodel creation is a one time process and relies on fast sampling through accurate parasitic-aware simulations. One of the targets of this dissertation is to minimize the sample size while retaining the accuracy of the model. in order to achieve this goal, different statistical sampling techniques are explored and applied to various AMS-SoC circuits. Also, different metamodel functions are explored for their …

This dissertation provides an innovative mechanism to collaborate two geographically separated people on a physical task and a novel method to measure Complexity Index (CI) and calculate Minimal Complexity Index (MCI) of a collaboration protocol. The protocol is represented as a structure, and the information content of it is measured in bits to understand the complex nature of the protocol. Using the complexity metrics, one can analyze the performance of a collaborative system and a collaboration protocol. Security and privacy of the consumers are vital while seeking remote help; this dissertation also provides a novel authorization framework for dynamic access control of resources on an input-constrained appliance used for completing the physical task. Using the innovative Collaborative Appliance for REmote-help (CARE) and with the support of a remotely located expert, fifty-nine subjects with minimal or no prior mechanical knowledge are able to elevate a car for replacing a tire in an average time of six minutes and 53 seconds and with an average protocol complexity of 171.6 bits. Moreover, thirty subjects with minimal or no prior plumbing knowledge are able to change the cartridge of a faucet in an average time of ten minutes and with an average protocol complexity …

There is an increased use of block-based programming environments in K-12 education and computing outreach activities to introduce novices to programming and computational thinking skills. However, despite their appealing design that allows students to focus on concepts rather than syntax, block-based programming by design is inaccessible to people with visual impairments and people who cannot use the mouse. In addition to this inaccessibility, little is known about the instructional experiences of students with visual impairments on current block-based programming environments. This dissertation addresses this gap by (1) investigating the challenges that students with visual impairments face on current block-based programming environments and (2) exploring ways in which we can use the keyboard and the screen reader to create block-based code. Through formal survey and interview studies with teachers of students with visual impairments and students with visual impairments, we identify several challenges faced by students with visual impairments on block-based programming environments. Using the knowledge of these challenges and building on prior work, we explore how to leverage the keyboard and the screen reader to improve the accessibility of block-based programming environments through a prototype of an accessible block-based programming library. In this dissertation, our empirical evaluations demonstrate that people …

In recent years, there have been incidents which decreased people's trust in some organizations and authorities responsible for ratings and accreditation. For a few prominent examples, there was a security breach at Equifax (2017), misconduct was found in the Standard & Poor's Ratings Services (2015), and the Accrediting Council for Independent Colleges and Schools (2022) validated some of the low-performing schools as delivering higher standards than they actually were. A natural solution to these types of issues is to decentralize the relevant trust management processes using blockchain technologies. The research problems which are tackled in this thesis consider the issue of trust in reputation for assessment and review credibility at different angles, in the context of blockchain applications. We first explored the following questions. How can we trust courses in one college to provide students with the type and level of knowledge which is needed in a specific workplace? Micro-accreditation on a blockchain was our solution, including using a peer-review system to determine the rigor of a course (through a consensus). Rigor is the level of difficulty in regard to a student's expected level of knowledge. Currently, we make assumptions about the quality and rigor of what is learned, but …

Students with upper body motor impairments, such as cerebral palsy, multiple sclerosis, ALS, etc., face challenges when learning to program in block-based programming environments, because these environments are highly dependent on the physical manipulation of a mouse or keyboard to drag and drop elements on the screen. In my dissertation, I make the block-based programming environment Blockly, accessible to students with upper body motor impairment by adding speech as an alternative form of input. This voice-enabled version of Blockly will reduce the need for the use of a mouse or keyboard, making it more accessible to students with upper body motor impairments. The voice-enabled Blockly system consists of the original Blockly application, a speech recognition API, predefined voice commands, and a custom function. Three user studies have been conducted, a preliminary study, a usability study, and an A/B test. These studies revealed a lot of information, such as the need for simpler, shorter, and more intuitive commands, the need to change the target audience, the shortcomings of speech recognition systems, etc. The feedback received from each study influenced design decisions at different phases. The findings also gave me insight into the direction I would like to go in the future. …

Voice over IP (VoIP) is a key enabling technology for the migration of circuit-switched PSTN architectures to packet-based IP networks. However, this migration is successful only if the present problems in IP networks are addressed before deploying VoIP infrastructure on a large scale. One of the important issues that the present VoIP networks face is the problem of unwanted calls commonly referred to as SPIT (spam over Internet telephony). Mostly, these SPIT calls are from unknown callers who broadcast unwanted calls. There may be unwanted calls from legitimate and known people too. In this case, the unwantedness depends on social proximity of the communicating parties. For detecting these unwanted calls, I propose a framework that analyzes incoming calls for unwanted behavior. The framework includes a VoIP spam detector (VSD) that analyzes incoming VoIP calls for spam behavior using trust and reputation techniques. The framework also includes a nuisance detector (ND) that proactively infers the nuisance (or reluctance of the end user) to receive incoming calls. This inference is based on past mutual behavior between the calling and the called party (i.e., caller and callee), the callee's presence (mood or state of mind) and tolerance in receiving voice calls from the …

Language can sometimes be an impediment in communication. Whether we are talking about people who speak different languages, students who are learning a new language, or people with language disorders, the understanding of linguistic representations in a given language requires a certain amount of knowledge that not everybody has. In this thesis, we propose "translation through pictures" as a means for conveying simple pieces of information across language barriers, and describe a system that can automatically generate pictorial representations for simple sentences. Comparative experiments conducted on visual and linguistic representations of information show that a considerable amount of understanding can be achieved through pictorial descriptions, with results within a comparable range of those obtained with current machine translation techniques. Moreover, a user study conducted around the pictorial translation system reveals that users found the system to generally produce correct word/image associations, and rate the system as interactive and intelligent.

This research proposes Stress-Lysis, iLog and SaYoPillow to automatically detect and monitor the stress levels of a person. To self manage psychological stress in the framework of smart healthcare, a deep learning based novel system (Stress-Lysis) is proposed in this dissertation. The learning system is trained such that it monitors stress levels in a person through human body temperature, rate of motion and sweat during physical activity. The proposed deep learning system has been trained with a total of 26,000 samples per dataset and demonstrates accuracy as high as 99.7%. The collected data are transmitted and stored in the cloud, which can help in real time monitoring of a person's stress levels, thereby reducing the risk of death and expensive treatments. The proposed system has the ability to produce results with an overall accuracy of 98.3% to 99.7%, is simple to implement and its cost is moderate. Chronic stress, uncontrolled or unmonitored food consumption, and obesity are intricately connected, even involving certain neurological adaptations. In iLog we propose a system which can not only monitor but also create awareness for the user of how much food is too much. iLog provides information on the emotional state of a person along …

New circuit design techniques that accommodate lower supply voltages necessary for portable systems need to be integrated into the semiconductor intellectual property (IP) core. Systems that once worked at 3.3 V or 2.5 V now need to work at 1.8 V or lower, without causing any performance degradation. Also, the fluctuation of device characteristics caused by process variation in nanometer technologies is seen as design yield loss. The numerous parasitic effects induced by layouts, especially for high-performance and high-speed circuits, pose a problem for IC design. Lack of exact layout information during circuit sizing leads to long design iterations involving time-consuming runs of complex tools. There is a strong need for low-power, high-performance, parasitic-aware and process-variation-tolerant circuit design. This dissertation proposes methodologies and techniques to achieve variability, power, performance, and parasitic-aware circuit designs. Three approaches are proposed: the single iteration automatic approach, the hybrid Monte Carlo and design of experiments (DOE) approach, and the corner-based approach. Widely used mixed-signal circuits such as analog-to-digital converter (ADC), voltage controlled oscillator (VCO), voltage level converter and active pixel sensor (APS) have been designed at nanoscale complementary metal oxide semiconductor (CMOS) and subjected to the proposed methodologies. The effectiveness of the proposed methodologies has …

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.