Abstract Probabilistic reasoning under uncertainty suits well to analysis of disease dynamics. The stochastic nature of disease progression is modeled by applying the principles of Bayesian learning. Bayesian learning predicts the disease progression, including prevalence and incidence, for a geographic region and demographic composition. Public health resources, prioritized by the order of risk levels of the population, will efficiently minimize the disease spread and curtail the epidemic at the earliest. A Bayesian network representing the outbreak of influenza and pneumonia in a geographic region is ported to a newer region with different demographic composition. Upon analysis for the newer region, the corresponding prevalence of influenza and pneumonia among the different demographic subgroups is inferred for the newer region. Bayesian reasoning coupled with disease timeline is used to reverse engineer an influenza outbreak for a given geographic and demographic setting. The temporal flow of the epidemic among the different sections of the population is analyzed to identify the corresponding risk levels. In comparison to spread vaccination, prioritizing the limited vaccination resources to the higher risk groups results in relatively lower influenza prevalence. HIV incidence in Texas from 1989-2002 is analyzed using demographic based epidemic curves. Dynamic Bayesian networks are integrated with …

Hand and arm gestures are a great way of communication when you don't want to be heard, quieter and often more reliable than whispering into a radio mike. In recent years hand gesture identification became a major active area of research due its use in various applications. The objective of my work is to develop an integrated sensor system, which will enable tactical squads and SWAT teams to communicate when there is absence of a Line of Sight or in the presence of any obstacles. The gesture set involved in this work is the standardized hand signals for close range engagement operations used by military and SWAT teams. The gesture sets involved in this work are broadly divided into finger movements and arm movements. The core components of the integrated sensor system are: Surface EMG sensors, Flex sensors and accelerometers. Surface EMG is the electrical activity produced by muscle contractions and measured by sensors directly attached to the skin. Bend Sensors use a piezo resistive material to detect the bend. The sensor output is determined by both the angle between the ends of the sensor as well as the flex radius. Accelerometers sense the dynamic acceleration and inclination in 3 …

Prompt and efficient intervention is vital in reducing casualty figures during epidemic outbreaks, disasters, sudden civil strife or terrorism attacks. This can only be achieved if there is a fit-for-purpose and location-specific emergency response plan in place, incorporating geographical, time and vehicular capacity constraints. In this research, a comprehensive emergency response model for situations of uncertainties (in locations' demand and available resources), typically obtainable in low-resource countries, is designed. It involves the development of algorithms for optimizing pre-and post-disaster activities. The studies result in the development of four models: (1) an adaptation of a machine learning clustering algorithm, for pre-positioning depots and emergency operation centers, which optimizes the placement of these depots, such that the largest geographical location is covered, and the maximum number of individuals reached, with minimal facility cost; (2) an optimization algorithm for routing relief distribution, using heterogenous fleets of vehicle, with considerations for uncertainties in humanitarian supplies; (3) a genetic algorithm-based route improvement model; and (4) a model for integrating possible new locations into the routing network, in real-time, using emergency severity ranking, with a high priority on the most-vulnerable population. The clustering approach to solving dept location-allocation problem produces a better time complexity, and the …

Intelligent transportation systems (ITS) are essential tools for traffic planning, analysis, and forecasting that can utilize the huge amount of traffic data available nowadays. In this work, we aggregated detailed traffic flow sensor data, Waze reports, OpenStreetMap (OSM) features, and weather data, from California Bay Area for 6 months. Using that data, we studied three novel ITS applications using convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The first experiment is an analysis of the relation between roadway shapes and accident occurrence, where results show that the speed limit and number of lanes are significant predictors for major accidents on highways. The second experiment presents a novel method for forecasting congestion severity using crowdsourced data only (Waze, OSM, and weather), without the need for traffic sensor data. The third experiment studies the improvement of traffic flow forecasting using accidents, number of lanes, weather, and time-related features, where results show significant performance improvements when the additional features where used.

Hateful content and offensive language are commonplace on social media platforms. Many surveys prove that high percentages of social media users experience online harassment. Previous efforts have been made to detect and remove online hate content automatically. However, removing users' content restricts free speech. A complementary strategy to address hateful content that does not interfere with free speech is to counter the hate with new content to divert the discourse away from the hate. In this dissertation, we complement the lack of previous work on counterhate arguments by analyzing and detecting them. Firstly, we study the relationships between hateful tweets and replies. Specifically, we analyze their fine-grained relationships by indicating whether the reply counters the hate, provides a justification, attacks the author of the tweet, or adds additional hate. The most obvious finding is that most replies generally agree with the hateful tweets; only 20% of them counter the hate. Secondly, we focus on the hate directed toward individuals and detect authentic counterhate arguments from online articles. We propose a methodology that assures the authenticity of the argument and its specificity to the individual of interest. We show that finding arguments in online articles is an efficient alternative compared to …

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 …

This dissertation presents a data-driven computational epidemic framework to simulate disease epidemics at global mass gatherings. The annual Muslim pilgrimage to Makkah, Saudi Arabia is used to demonstrate the simulation and analysis of various disease transmission scenarios throughout the different stages of the event from the arrival to the departure of international participants. The proposed agent-based epidemic model efficiently captures the demographic, spatial, and temporal heterogeneity at each stage of the global event of Hajj. Experimental results indicate the substantial impact of the demographic and mobility patterns of the heterogeneous population of pilgrims on the progression of the disease spread in the different stages of Hajj. In addition, these simulations suggest that the differences in the spatial and temporal settings in each stage can significantly affect the dynamic of the disease. Finally, the epidemic simulations conducted at the different stages in this dissertation illustrate the impact of the differences between the duration of each stage in the event and the length of the infectious and latent periods. This research contributes to a better understanding of epidemic modeling in the context of global mass gatherings to predict the risk of disease pandemics caused by associated international travel. The computational modeling and …

The design of VLSI electronic circuits can be achieved at many different abstraction levels starting from system behavior to the most detailed, physical layout level. As the number of transistors in VLSI circuits is increasing, the complexity of the design is also increasing, and it is now beyond human ability to manage. Hence CAD (Computer Aided design) or EDA (Electronic Design Automation) tools are involved in the design. EDA or CAD tools automate the design, verification and testing of these VLSI circuits. In today’s market, there are many EDA tools available. However, they are very expensive and require high-performance platforms. One of the key challenges today is to select appropriate CAD or EDA tools which are open-source for academic purposes. This thesis provides a detailed examination of an open-source EDA tool called Electric VLSI Design system. An excellent and efficient CAD tool useful for students and teachers to implement ideas by modifying the source code, Electric fulfills these requirements. This thesis' primary objective is to explain the Electric software features and architecture and to provide various digital and analog designs that are implemented by this software for educational purposes. Since the choice of an EDA tool is based on the …

Extensive research has been conducted by the grid community to enable large-scale collaborations in pre-configured environments. grid collaborations can vary in scale and motivation resulting in a coarse classification of grids: national grid, project grid, enterprise grid, and volunteer grid. Despite the differences in scope and scale, all the traditional grids in practice share some common assumptions. They support mutually collaborative communities, adopt a centralized control for membership, and assume a well-defined non-changing collaboration. To support grid applications that do not confirm to these assumptions, we propose the concept of ad hoc grids. In the context of this research, we propose a novel architecture for ad hoc grids that integrates a suite of component frameworks. Specifically, our architecture combines the community management framework, security framework, abstraction framework, quality of service framework, and reputation framework. The overarching objective of our integrated architecture is to support a variety of grid applications in a self-controlled fashion with the help of a self-organizing ad hoc community. We introduce mechanisms in our architecture that successfully isolates malicious elements from the community, inherently improving the quality of grid services and extracting deterministic quality assurances from the underlying infrastructure. We also emphasize on the technology-independence of our …

Many of the contemporary routing algorithms use simple mechanisms such as flooding or broadcasting to disseminate the routing information available to them. Such routing algorithms cause significant network resource overhead due to the large number of messages generated at each host/router throughout the route update process. Many of these messages are wasteful since they do not contribute to the route discovery process. Reducing the resource overhead may allow for several algorithms to be deployed in a wide range of networks (wireless and ad-hoc) which require a simple routing protocol due to limited availability of resources (memory and bandwidth). Motivated by the need to reduce the resource overhead associated with routing algorithms a new implementation of distance vector routing algorithm using an agent-based paradigm known as Agent-based Distance Vector Routing (ADVR) has been proposed. In ADVR, the ability of route discovery and message passing shifts from the nodes to individual agents that traverse the network, co-ordinate with each other and successively update the routing tables of the nodes they visit.

With the increasing demand on wireless and portable devices, the radio frequency front end blocks are required to feature properties such as wideband, high frequency, multiple operating frequencies, low cost and compact size. However, the current radio frequency system blocks are designed by combining several individual frequency band blocks into one functional block, which increase the cost and size of devices. To address these issues, it is important to develop novel approaches to further advance the current design methodologies in both space and spectrum domains. In recent years, the concept of artificial materials has been proposed and studied intensively in RF/Microwave, Terahertz, and optical frequency range. It is a combination of conventional materials such as air, wood, metal and plastic. It can achieve the material properties that have not been found in nature. Therefore, the artificial material (i.e. meta-materials) provides design freedoms to control both the spectrum performance and geometrical structures of radio frequency front end blocks and other high frequency systems. In this dissertation, several artificial materials are proposed and designed by different methods, and their applications to different high frequency components and circuits are studied. First, quasi-conformal mapping (QCM) method is applied to design plasmonic wave-adapters and couplers …

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 …

In recent years, brain computer interfaces (BCIs) have gained popularity in non-medical domains such as the gaming, entertainment, personal health, and marketing industries. A growing number of companies offer various inexpensive consumer grade BCIs and some of these companies have recently introduced the concept of BCI "App stores" in order to facilitate the expansion of BCI applications and provide software development kits (SDKs) for other developers to create new applications for their devices. The BCI applications access to users' unique brainwave signals, which consequently allows them to make inferences about users' thoughts and mental processes. Since there are no specific standards that govern the development of BCI applications, its users are at the risk of privacy breaches. In this work, we perform first comprehensive analysis of BCI App stores including software development kits (SDKs), application programming interfaces (APIs), and BCI applications w.r.t privacy issues. The goal is to understand the way brainwave signals are handled by BCI applications and what threats to the privacy of users exist. Our findings show that most applications have unrestricted access to users' brainwave signals and can easily extract private information about their users without them even noticing. We discuss potential privacy threats posed by …

Component failures can result in catastrophic behaviors in safety critical embedded systems, sometimes resulting in loss of life. Component failures can be treated as off nominal behaviors (ONBs) with respect to the components and sub systems involved in an embedded system. A lot of research is being carried out to tackle the problem of ONBs. These approaches are mainly focused on the states (i.e., desired and undesired states of a system at a given point of time to detect ONBs). In this paper, an approach is discussed to detect component failures and critical components of an embedded system. The approach is based on fault tree analysis (FTA), applied to the requirements specification of embedded systems at design time to find out the relationship between individual component failures and overall system failure. FTA helps in determining both qualitative and quantitative relationship between component failures and system failure. Analyzing the system at design time helps in detecting component failures and critical components and helps in devising strategies to mitigate component failures at design time and improve overall safety and reliability of a system.

Virtual teams in industry are increasingly being used to develop software, create products, and accomplish tasks. However, analyzing those collaborations under same-time/different-place conditions is well-known to be difficult. In order to overcome some of these challenges, this research was concerned with the study of collaboration-based, content-based and temporal measures and their ability to predict cohesion within global software development projects. Messages were collected from three software development projects that involved students from two different countries. The similarities and quantities of these interactions were computed and analyzed at individual and group levels. Results of interaction-based metrics showed that the collaboration variables most related to Task Cohesion were Linguistic Style Matching and Information Exchange. The study also found that Information Exchange rate and Reply rate have a significant and positive correlation to Task Cohesion, a factor used to describe participants' engagement in the global software development process. This relation was also found at the Group level. All these results suggest that metrics based on rate can be very useful for predicting cohesion in virtual groups. Similarly, content features based on communication categories were used to improve the identification of Task Cohesion levels. This model showed mixed results, since only Work similarity and …

Partial order planning is an important approach that solves planning problems without completely specifying the orderings between the actions in the plan. This property provides greater flexibility in executing plans; hence making the partial order planners a preferred choice over other planning methodologies. However, in order to find partially ordered plans, partial order planners perform a search in plan space rather than in space of world states and an uninformed search in plan space leads to poor efficiency. In this thesis, I discuss applying a reinforcement learning method, called First-visit Monte Carlo method, to partial order planning in order to design agents which do not need any training data or heuristics but are still able to make informed decisions in plan space based on experience. Communicating effectively with the agent is crucial in reinforcement learning. I address how this task was accomplished in plan space and the results from an evaluation of a blocks world test bed.

Internet technology has affected humans' lives in many disciplines. The search engine is one of the most important Internet tools in that it allows people to search for what they want. Search queries entered in a web search engine can be used to predict dengue incidence. This vector borne disease causes severe illness and kills a large number of people every year. This dissertation utilizes the capabilities of search queries related to dengue and climate to forecast the number of dengue cases. Several machine learning techniques are applied for data analysis, including Multiple Linear Regression, Artificial Neural Networks, and the Seasonal Autoregressive Integrated Moving Average. Predictive models produced from these machine learning methods are measured for their performance to find which technique generates the best model for dengue prediction. The results of experiments presented in this dissertation indicate that search query data related to dengue and climate can be used to forecast the number of dengue cases. The performance measurement of predictive models shows that Artificial Neural Networks outperform the others. These results will help public health officials in planning to deal with the outbreaks.

Edge computing and CAV (Connected Autonomous Vehicle) fields can work as a team. With the short latency and high responsiveness of edge computing, it is a better fit than cloud computing in the CAV field. Moreover, containerized applications are getting rid of the annoying procedures for setting the required environment. So that deployment of applications on new machines is much more user-friendly than before. Therefore, this paper proposes a framework developed for the CAV edge computing scenario. This framework consists of various programs written in different languages. The framework uses Docker technology to containerize these applications so that the deployment could be simple and easy. This framework consists of two parts. One is for the vehicle on-board unit, which exposes data to the closest edge device and receives the output generated by the edge device. Another is for the edge device, which is responsible for collecting and processing big load of data and broadcasting output to vehicles. So the vehicle does not need to perform the heavyweight tasks that could drain up the limited power.

As video capturing devices become more ubiquitous from surveillance cameras to smart phones, the demand of automated video analysis is increasing as never before. One obstacle in this process is to efficiently locate where a human operator’s attention should be, and another is to determine the specific types of activities or actions without ambiguity. It is the special interest of this dissertation to locate spatial and temporal regions of interest in videos and to develop a better action representation for video-based activity analysis. This dissertation follows the scheme of “locating then recognizing” activities of interest in videos, i.e., locations of potentially interesting activities are estimated before performing in-depth analysis. Theoretical properties of regions of interest in videos are first exploited, based on which a unifying framework is proposed to locate both spatial and temporal regions of interest with the same settings of parameters. The approach estimates the distribution of motion based on 3D structure tensors, and locates regions of interest according to persistent occurrences of low probability. Two contributions are further made to better represent the actions. The first is to construct a unifying model of spatio-temporal relationships between reusable mid-level actions which bridge low-level pixels and high-level activities. Dense …

Possession is an asymmetric semantic relation between two entities, where one entity (the possessee) belongs to the other entity (the possessor). Automatically extracting possessions are useful in identifying skills, recommender systems and in natural language understanding. Possessions can be found in different communication modalities including text, images, videos, and audios. In this dissertation, I elaborate on the techniques I used to extract possessions. I begin with extracting possessions at the sentence level including the type and temporal anchors. Then, I extract the duration of possession and co-possessions (if multiple possessors possess the same entity). Next, I extract possessions from an entire Wikipedia article capturing the change of possessors over time. I extract possessions from social media including both text and images. Finally, I also present dense annotations generating possession timelines. I present separate datasets, detailed corpus analysis, and machine learning models for each task described above.

This research addresses the problem of automatic keyphrase extraction from large documents and back of the book indexing. The potential benefits of automating this process are far reaching, from improving information retrieval in digital libraries, to saving countless man-hours by helping professional indexers creating back of the book indexes. The dissertation introduces a new methodology to evaluate automated systems, which allows for a detailed, comparative analysis of several techniques for keyphrase extraction. We introduce and evaluate both supervised and unsupervised techniques, designed to balance the resource requirements of an automated system and the best achievable performance. Additionally, a number of novel features are proposed, including a statistical informativeness measure based on chi statistics; an encyclopedic feature that taps into the vast knowledge base of Wikipedia to establish the likelihood of a phrase referring to an informative concept; and a linguistic feature based on sophisticated semantic analysis of the text using current theories of discourse comprehension. The resulting keyphrase extraction system is shown to outperform the current state of the art in supervised keyphrase extraction by a large margin. Moreover, a fully automated back of the book indexing system based on the keyphrase extraction system was shown to lead to back …

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.

Dengue mitigation is a major research area among scientist who are working towards an effective management of the dengue epidemic. An effective dengue mitigation requires several other important components. These components include an accurate epidemic modeling, an efficient epidemic prediction, and an efficient resource allocation for controlling of the spread of the dengue disease. Past studies assumed homogeneous response pattern of the dengue epidemic to climate conditions throughout the regions. The dengue epidemic is climate dependent and also it is geographically dependent. A global model is not sufficient to capture the local variations of the epidemic. We propose a novel method of epidemic modeling considering local variation and that uses micro ensemble of regressors for each region. There are three regressors that are used in the construction of the ensemble. These are support vector regression, ordinary least square regression, and a k-nearest neighbor regression. The best performing regressors get selected into the ensemble. The proposed ensemble determines the risk of dengue epidemic in each region in advance. The risk is then used in risk-based resource allocation. The proposing resource allocation is built based on the genetic algorithm. The algorithm exploits the genetic algorithm with major modifications to its main components, …

In this work, we leverage the recent developments in computer science to address several of the challenges in current literature-based discovery (LBD) solutions. First, LBD solutions cannot use semantics or are too computational complex. To solve the problems we propose a generative model OverlapLDA based on topic modeling, which has been shown both effective and efficient in extracting semantics from a corpus. We also introduce an inference method of OverlapLDA. We conduct extensive experiments to show the effectiveness and efficiency of OverlapLDA in LBD. Second, we expand LBD to a more complex and realistic setting. The settings are that there can be more than one concept connecting the input concepts, and the connectivity pattern between concepts can also be more complex than a chain. Current LBD solutions can hardly complete the LBD task in the new setting. We simplify the hypotheses as concept sets and propose LBDSetNet based on graph neural networks to solve this problem. We also introduce different training schemes based on self-supervised learning to train LBDSetNet without relying on comprehensive labeled hypotheses that are extremely costly to get. Our comprehensive experiments show that LBDSetNet outperforms strong baselines on simple hypotheses and addresses complex hypotheses.