In developing countries, acute respiratory infections (ARIs) are responsible for two million deaths per year. Most victims are children who are less than 5 years old. Pneumonia kills 5000 children per day. The statistics for cardiovascular diseases (CVDs) are even more alarming. According to a 2009 report from the World Health Organization (WHO), CVDs kill 17 million people per year. In many resource-poor parts of the world such as India and China, many people are unable to access cardiologists, pulmonologists, and other specialists. Hence, low skilled health professionals are responsible for screening people for ARIs and CVDs in these areas. For example, in the rural areas of the Philippines, there is only one doctor for every 10,000 people. By contrast, the United States has one doctor for every 500 Americans. Due to advances in technology, it is now possible to use a smartphone for audio recording, signal processing, and machine learning. In my thesis, I have developed an Android application named Smart Auscultation. Auscultation is a process in which physicians listen to heart and lung sounds to diagnose disorders. Cardiologists spend years mastering this skill. The Smart Auscultation application is capable of recording and classifying heart sounds, and can be used by public or clinical health workers. This application can detect abnormal heart sounds with up to 92-98% accuracy. In addition, the application can record, but not yet classify, lung sounds. This application will be able to help save thousands of lives by allowing anyone to identify abnormal heart and lung sounds.
Mobile applications are growing in popularity and pose new problems in the area of software testing. In particular, mobile applications heavily depend upon user interactions and a dynamically changing environment of system events. In this thesis, we focus on user-driven events and use Q-learning, a reinforcement machine learning algorithm, to generate tests for Android applications under test (AUT). We implement a framework that automates the generation of GUI test cases by using our Q-learning approach and compare it to a uniform random (UR) implementation. A novel feature of our approach is that we generate user-driven event sequences through the GUI, without the source code or the model of the AUT. Hence, considerable amount of cost and time are saved by avoiding the need for model generation for generating the tests. Our results show that the systematic path exploration used by Q-learning results in higher average code coverage in comparison to the uniform random approach.
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 current practices used in BCI App stores and then describe some countermeasures that could be used to mitigate the privacy threats. Also, develop a prototype which gives the BCI app users a choice to restrict their brain signal dynamically.
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 Social rate were found to be correlated with Task Cohesion. This result can be explained by how a group's cohesiveness is often associated with fairness and trust, and that these two factors are often achieved by increased social and work communications. Also, at a group-level, all models were found correlated to Task Cohesion, specifically, Similarity+Rate, which suggests that models that include social and work communication categories are also good predictors of team cohesiveness. Finally, temporal interaction similarity measures were calculated to assess their prediction capabilities in a global setting. Results showed a significant negative correlation between the Pacing Rate and ...
This work describes an approach to determine whether people participate in the events they tweet about. Specifically, we determine whether people are participants in events with respect to the tweet timestamp. We target all events expressed by verbs in tweets, including past, present and events that may occur in future. We define event participant as people directly involved in an event regardless of whether they are the agent, recipient or play another role. We present an annotation effort, guidelines and quality analysis with 1,096 event mentions. We discuss the label distributions and event behavior in the annotated corpus. We also explain several features used and a standard supervised machine learning approach to automatically determine if and when the author is a participant of the event in the tweet. We discuss trends in the results obtained and devise important conclusions.
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.
In recent years, social media platforms such as Twitter and Facebook have emerged as effective tools for broadcasting messages worldwide during disaster events. With millions of messages posted through these services during such events, it has become imperative to identify valuable information that can help the emergency responders to develop effective relief efforts and aid victims. Many studies implied that the role of social media during disasters is invaluable and can be incorporated into emergency decision-making process. However, due to the "big data" nature of social media, it is very labor-intensive to employ human resources to sift through social media posts and categorize/classify them as useful information. Hence, there is a growing need for machine intelligence to automate the process of extracting useful information from the social media data during disaster events. This dissertation addresses the following questions: In a social media stream of messages, what is the useful information to be extracted that can help emergency response organizations to become more situationally aware during and following a disaster? What are the features (or patterns) that can contribute to automatically identifying messages that are useful during disasters? We explored a wide variety of features in conjunction with supervised learning algorithms to automatically identify messages that are useful during disaster events. The feature design includes sentiment features to extract the geo-mapped sentiment expressed in tweets, as well as tweet-content and user detail features to predict the likelihood of the information contained in a tweet to be quickly spread in the network. Further experimentation is carried out to see how these features help in identifying the informative tweets and filter out those tweets that are conversational in nature.
This thesis describes an approach for object recognition using the chordiogram shape-based descriptor. Global shape representations are highly susceptible to clutter generated due to the background or other irrelevant objects in real-world images. To overcome the problem, we aim to extract precise object shape using superpixel segmentation, perceptual grouping, and connected components. The employed shape descriptor chordiogram is based on geometric relationships of chords generated from the pairs of boundary points of an object. The chordiogram descriptor applies holistic properties of the shape and also proven suitable for object detection and digit recognition mechanisms. Additionally, it is translation invariant and robust to shape deformations. In spite of such excellent properties, chordiogram is not scale-invariant. To this end, we propose scale invariant chordiogram descriptors and intend to achieve a similar performance before and after applying scale invariance. Our experiments show that we achieve similar performance with and without scale invariance for silhouettes and real world object images. We also show experiments at different scales to confirm that we obtain scale invariance for chordiogram.
The outbreak of the Ebola virus was declared a Public Health Emergency of International Concern by the World Health Organisation (WHO). Due to the complex nature of the outbreak, the Centers for Disease Control and Prevention (CDC) had created interim guidance for monitoring people potentially exposed to Ebola and for evaluating their intended travel and restricting the movements of carriers when needed. Tools to evaluate the risk of individuals and groups of individuals contracting the disease could mitigate the growing anxiety and fear. The goal is to understand and analyze the nature of risk an individual would face when he/she comes in contact with a carrier. This thesis presents a tool that makes use of contextual data intelligence to predict the risk factor of individuals who come in contact with the carrier.
Epidemiologists rely on human interaction networks for determining states and dynamics of disease propagations in populations. However, such networks are empirical snapshots of the past. It will greatly benefit if human interaction networks are statistically predicted and dynamically created while an epidemic is in progress. We develop an application framework for the generation of human interaction networks and running epidemiological processes utilizing research on human mobility patterns and agent-based modeling. The interaction networks are dynamically constructed by incorporating different types of Random Walks and human rules of engagements. We explore the characteristics of the created network and compare them with the known theoretical and empirical graphs. The dependencies of epidemic dynamics and their outcomes on patterns and parameters of human motion and motives are encountered and presented through this research. This work specifically describes how the types and parameters of random walks define properties of generated graphs. We show that some configurations of the system of agents in random walk can produce network topologies with properties similar to small-world networks. Our goal is to find sets of mobility patterns that lead to empirical-like networks. The possibility of phase transitions in the graphs due to changes in the parameterization of agent walks is the focus of this research as this knowledge can lead to the possibility of disruptions to disease diffusions in populations. This research shall facilitate work of public health researchers to predict the magnitude of an epidemic and estimate resources required for mitigation.
Automatically generating questions from text for educational purposes is an active research area in natural language processing. The automatic question generation system accompanying this dissertation is MARGE, which is a recursive acronym for: MARGE automatically reads generates and evaluates. MARGE generates questions from both individual sentences and the passage as a whole, and is the first question generation system to successfully generate meaningful questions from textual units larger than a sentence. Prior work in automatic question generation from text treats a sentence as a string of constituents to be rearranged into as many questions as allowed by English grammar rules. Consequently, such systems overgenerate and create mainly trivial questions. Further, none of these systems to date has been able to automatically determine which questions are meaningful and which are trivial. This is because the research focus has been placed on NLG at the expense of NLU. In contrast, the work presented here infuses the questions generation process with natural language understanding. From the input text, MARGE creates a meaning analysis representation for each sentence in a passage via the DeconStructure algorithm presented in this work. Questions are generated from sentence meaning analysis representations using templates. The generated questions are automatically evaluated for question quality and importance via a ranking algorithm.
The use of electroencephalogram (EEG), an electrophysiological monitoring method for recording the brain activity, for authentication has attracted the interest of researchers for over a decade. In addition to exhibiting qualities of biometric-based authentication, they are revocable, impossible to mimic, and resistant to coercion attacks. However, EEG signals carry a wealth of information about an individual and can reveal private information about the user. This brings significant privacy issues to EEG-based authentication systems as they have access to raw EEG signals. This thesis proposes a privacy-preserving EEG-based authentication system that preserves the privacy of the user by not revealing the raw EEG signals while allowing the system to authenticate the user accurately. In that, perceptual hashing is utilized and instead of raw EEG signals, their perceptually hashed values are used in the authentication process. In addition to describing the authentication process, algorithms to compute the perceptual hash are developed based on two feature extraction techniques. Experimental results show that an authentication system using perceptual hashing can achieve performance comparable to a system that has access to raw EEG signals if enough EEG channels are used in the process. This thesis also presents a security analysis to show that perceptual hashing can prevent information leakage.
The focus of this research is on the development of a real time application that uses a low cost EEG headset to measure a player's state of mind while they play a video game. Using data collected using the Emotiv EPOC headset, various EEG processing techniques are tested to find ways of measuring a person's engagement and arousal levels. The ability to measure a person's engagement and arousal levels provide an opportunity to develop a model that monitor a person's flow while playing video games. Identifying when certain events occur, like when the player dies, will make it easier to identify when a player has left a state of flow. The real time application Brainwave captures data from the wireless Emotiv EPOC headset. Brainwave converts the raw EEG data into more meaningful brainwave band frequencies. Utilizing the brainwave frequencies the program trains multiple machine learning algorithms with data designed to identify when the player dies. Brainwave runs while the player plays through a video gaming monitoring their engagement and arousal levels for changes that cause the player to leave a state of flow. Brainwave reports to researchers and developers when the player dies along with the identification of the players exit of the state of flow.
The objective of this thesis is to design a model for a multi global navigation satellite system using Simulink. It explains a design procedure which includes the models for transmitter and receiver for two different navigation systems. To overcome the problem, where less number of satellites are visible to determine location degrades the performance of any positioning system significantly, this research has done to make use of multi GNSS satellite signals in one navigation receiver.
Managing large-scale dynamical systems (e.g., transportation systems, complex information systems, and power networks, etc.) in real-time is very challenging considering their complicated system dynamics, intricate network interactions, large scale, and especially the existence of various uncertainties. To address this issue, intelligent techniques which can quickly design decision-making strategies that are robust to uncertainties are needed. This dissertation aims to conquer these challenges by exploring a data-driven decision-making framework, which leverages big-data techniques and scalable uncertainty evaluation approaches to quickly solve optimal control problems. In particular, following techniques have been developed along this direction: 1) system modeling approaches to simplify the system analysis and design procedures for multiple applications; 2) effective simulation and analytical based approaches to efficiently evaluate system performance and design control strategies under uncertainty; and 3) big-data techniques that allow some computations of control strategies to be completed offline. These techniques and tools for analysis, design and control contribute to a wide range of applications including air traffic flow management, complex information systems, and airborne networks.
With the introduction of 4G LTE, multiple new technologies were introduced. MIMO is one of the important technologies introduced with fourth generation. The main MIMO modes used in LTE are open loop and closed loop spatial multiplexing modes. This thesis develops an algorithm to calculate the threshold values of UE speed and SNR that is required to implement a switching algorithm which can switch between different MIMO modes for a UE based on the speed and channel conditions (CSI). Specifically, this thesis provides the values of UE speed and SNR at which we can get better results by switching between open loop and closed loop MIMO modes and then be scheduled in sub-channels accordingly. Thus, the results can be used effectively to get better channel capacity with less ISI. The main objectives of this thesis are: to determine the type of MIMO mode suitable for a UE with certain speed, to determine the effects of SNR on selection of MIMO modes, and to design and implement a scheduling algorithm to enhance channel capacity.
Automatic text summarization and keyphrase extraction are two interesting areas of research which extend along natural language processing and information retrieval. They have recently become very popular because of their wide applicability. Devising generic techniques for these tasks is challenging due to several issues. Yet we have a good number of intelligent systems performing the tasks. As different systems are designed with different perspectives, evaluating their performances with a generic strategy is crucial. It has also become immensely important to evaluate the performances with minimal human effort. In our work, we focus on designing a relativized scale for evaluating different algorithms. This is our major contribution which challenges the traditional approach of working with an absolute scale. We consider the impact of some of the environment variables (length of the document, references, and system-generated outputs) on the performance. Instead of defining some rigid lengths, we show how to adjust to their variations. We prove a mathematically sound baseline that should work for all kinds of documents. We emphasize automatically determining the syntactic well-formedness of the structures (sentences). We also propose defining an equivalence class for each unit (e.g. word) instead of the exact string matching strategy. We show an evaluation approach that considers the weighted relatedness of multiple references to adjust to the degree of disagreements between the gold standards. We publish the proposed approach as a free tool so that other systems can use it. We have also accumulated a dataset (scientific articles) with a reference summary and keyphrases for each document. Our approach is applicable not only for evaluating single-document based tasks but also for evaluating multiple-document based tasks. We have tested our evaluation method for three intrinsic tasks (taken from DUC 2004 conference), and in all three cases, it correlates positively with ROUGE. Based on our experiments ...
The history of mitigation programs to contain vector-borne diseases is a story of successes and failures. Due to the complex interplay among multiple factors that determine disease dynamics, the general principles for timely and specific intervention for incidence reduction or eradication of life-threatening diseases has yet to be determined. This research discusses computational methods developed to assist in the understanding of complex relationships affecting vector-borne disease dynamics. A computational framework to assist public health practitioners with exploring the dynamics of vector-borne diseases, such as malaria and dengue in homogenous and heterogeneous populations, has been conceived, designed, and implemented. The framework integrates a stochastic computational model of interactions to simulate horizontal disease transmission. The intent of the computational modeling has been the integration of stochasticity during simulation of the disease progression while reducing the number of necessary interactions to simulate a disease outbreak. While there are improvements in the computational time reducing the number of interactions needed for simulating disease dynamics, the realization of interactions can remain computationally expensive. Using multi-threading technology to improve performance upon the original computational model, multi-threading experimental results have been tested and reported. In addition, to the contact model, the modeling of biological processes specific to the corresponding pathogen-carrier vector to increase the specificity of the vector-borne disease has been integrated. Last, automation for requesting, retrieving, parsing, and storing specific weather data and geospatial information from federal agencies to study the differences between homogenous and heterogeneous populations has been implemented.
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.
The continuous expansion of technology, broadband connectivity and the wide range of new devices in the IoT cause serious concerns regarding privacy and security. In addition, in the IoT a key challenge is the storage and management of massive data streams. For example, there is always the demand for acceptable size with the highest quality possible for images to meet the rapidly increasing number of multimedia applications. The effort in this dissertation contributes to the resolution of concerns related to the security and compression functions in image communications in the Internet of Thing (IoT), due to the fast of evolution of IoT. This dissertation proposes frameworks for a secure digital camera in the IoT. The objectives of this dissertation are twofold. On the one hand, the proposed framework architecture offers a double-layer of protection: encryption and watermarking that will address all issues related to security, privacy, and digital rights management (DRM) by applying a hardware architecture of the state-of-the-art image compression technique Better Portable Graphics (BPG), which achieves high compression ratio with small size. On the other hand, the proposed framework of SBPG is integrated with the Digital Camera. Thus, the proposed framework of SBPG integrated with SDC is suitable for high performance imaging in the IoT, such as Intelligent Traffic Surveillance (ITS) and Telemedicine. Due to power consumption, which has become a major concern in any portable application, a low-power design of SBPG is proposed to achieve an energy- efficient SBPG design. As the visual quality of the watermarked and compressed images improves with larger values of PSNR, the results show that the proposed SBPG substantially increases the quality of the watermarked compressed images. Higher value of PSNR also shows how robust the algorithm is to different types of attack. From the results obtained for the energy- efficient SBPG ...
The human brain acts as an intelligent sensor by helping in effective signal communication and execution of logical functions and instructions, thus, coordinating all functions of the human body. More importantly, it shows the potential to combine prior knowledge with adaptive learning, thus ensuring constant improvement. These qualities help the brain to interact efficiently with both, the body (brain-body) as well as the environment (brain-environment). This dissertation attempts to apply the brain-body-environment interactions (BBEI) to elevate human existence and enhance our day-to-day experiences. For instance, when one stepped out of the house in the past, one had to carry keys (for unlocking), money (for purchasing), and a phone (for communication). With the advent of smartphones, this scenario changed completely and today, it is often enough to carry just one's smartphone because all the above activities can be performed with a single device. In the future, with advanced research and progress in BBEI interactions, one will be able to perform many activities by dictating it in one's mind without any physical involvement. This dissertation aims to shift the paradigm of existing brain-computer-interfaces from just ‘control' to ‘monitor, control, enhance, and restore' in three main areas - healthcare, transportation safety, and cryptography. In healthcare, measures were developed for understanding brain-body interactions by correlating cerebral autoregulation with brain signals. The variation in estimated blood flow of brain (obtained through EEG) was detected with evoked change in blood pressure, thus, enabling EEG metrics to be used as a first hand screening tool to check impaired cerebral autoregulation. To enhance road safety, distracted drivers' behavior in various multitasking scenarios while driving was identified by significant changes in the time-frequency spectrum of the EEG signals. A distraction metric was calculated to rank the severity of a distraction task that can be used as an intuitive measure ...
Like cell to the human body, transistors are the basic building blocks of any electronics circuits. Silicon has been the industries obvious choice for making transistors. Transistors with large size occupy large chip area, consume lots of power and the number of functionalities will be limited due to area constraints. Thus to make the devices smaller, smarter and faster, the transistors are aggressively scaled down in each generation. Moore's law states that the transistors count in any electronic circuits doubles every 18 months. Following this Moore's law, the transistor has already been scaled down to 14 nm. However there are limitations to how much further these transistors can be scaled down. Particularly below 10 nm, these silicon based transistors hit the fundamental limits like loss of gate control, high leakage and various other short channel effects. Thus it is not possible to favor the silicon transistors for future electronics applications. As a result, the research has shifted to new device concepts and device materials alternative to silicon. Carbon is the next abundant element found in the Earth and one of such carbon based nanomaterial is graphene. Graphene when extracted from Graphite, the same material used as the lid in pencil, have a tremendous potential to take future electronics devices to new heights in terms of size, cost and efficiency. Thus after its first experimental discovery of graphene in 2004, graphene has been the leading research area for both academics as well as industries. This dissertation is focused on the analysis and optimization of graphene based circuits for future electronics. The first part of this dissertation considers graphene based transistors for analog/radio frequency (RF) circuits. In this section, a dual gate Graphene Field Effect Transistor (GFET) is considered to build the case study circuits like voltage controlled oscillator (VCO) and low ...
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.
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 efficiency and functions that it can provide, this thesis explains all the analysis and synthesis tools that electric provides and how efficient they are. Hence, this thesis is of benefit for students and teachers that choose Electric as their open-source EDA tool for educational purposes.
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.
Electrical energy is used at approximately the rate of 15 Terawatts world-wide. Generating this much energy has become a primary concern for all nations. There are many ways of generating energy among which the most commonly used are non-renewable and will extinct much sooner than expected. Very active research is going on both to increase the use of renewable energy sources and to use the available energy with more efficiency. Among these sources, solar energy is being considered as the most abundant and has received high attention. The mobile phone has become one of the basic needs of modern life, with almost every human being having one.Individually a mobile phone consumes little power but collectively this becomes very large. This consideration motivated the research undertaken in this masters thesis. The objective of this thesis is to design a model for solar power based charging circuits for mobile phone using Simulink(R). This thesis explains a design procedure of solar power based mobile charger circuit using Simulink(R) which includes the models for the photo-voltaic array, maximum power point tracker, pulse width modulator, DC-DC converter and a battery.The first part of the thesis concentrates on electron level behavior of a solar cell, its structure and its electrical model.The second part is to design an array of solar cells to generate the desired output.Finally, the third part is to design a DC-DC converter which can stabilize and provide the required input to the battery with the help of the maximum power point tracker and pulse width modulation.The obtained DC-DC converter is adjustable to meet the requirements of the battery. This design is aimed at charging a lithium ion battery with nominal voltage of 3.7 V, which can be taken as baseline to charge different types of batteries with different nominal voltages.
In modern society, computers are primarily interacted with via keyboards, touch screens, voice recognition, video analysis, and many others. For certain applications, these methods may be the most efficient interface. However, there are applications that we can conceive where a more natural interface could be convenient and connect humans and computers in a more intuitive and natural way. These applications are gesture recognition systems and range from the interpretation of sign language by a computer to virtual reality control. This Thesis proposes a gesture recognition system that primarily uses accelerometers to capture gestures from a tactical communications application. A segmentation algorithm is developed based on the accelerometer energy to segment these gestures from an input sequence. Using signal processing and machine learning techniques, the segments are reduced to mathematical features and classified with support vector machines. Experimental results show that the system achieves an overall gesture recognition accuracy of 98.9%. Additional methods, such as non-gesture recognition/suppression, are also proposed and tested.
There are several types of disorders that affect our colon’s ability to function properly such as colorectal cancer, ulcerative colitis, diverticulitis, irritable bowel syndrome and colonic polyps. Automatic detection of these diseases would inform the endoscopist of possible sub-optimal inspection during the colonoscopy procedure as well as save time during post-procedure evaluation. But existing systems only detects few of those disorders like colonic polyps. In this dissertation, we address the automatic detection of another important disorder called ulcerative colitis. We propose a novel texture feature extraction technique to detect the severity of ulcerative colitis in block, image, and video levels. We also enhance the current informative frame filtering methods by detecting water and bubble frames using our proposed technique. Our feature extraction algorithm based on accumulation of pixel value difference provides better accuracy at faster speed than the existing methods making it highly suitable for real-time systems. We also propose a hybrid approach in which our feature method is combined with existing feature method(s) to provide even better accuracy. We extend the block and image level detection method to video level severity score calculation and shot segmentation. Also, the proposed novel feature extraction method can detect water and bubble frames in colonoscopy videos with very high accuracy in significantly less processing time even when clustering is used to reduce the training size by 10 times.
In order to run and manage resource-intensive high-performance applications, large-scale computing and storage platforms have been evolving rapidly in various domains in both academia and industry. The energy expenditure consumed to operate and maintain these cloud computing infrastructures is a major factor to influence the overall profit and efficiency for most cloud service providers. Moreover, considering the mitigation of environmental damage from excessive carbon dioxide emission, the amount of power consumed by enterprise-scale data centers should be constrained for protection of the environment.Generally speaking, there exists a trade-off between power consumption and application performance in large-scale computing systems and how to balance these two factors has become an important topic for researchers and engineers in cloud and HPC communities. Therefore, minimizing the power usage while satisfying the Service Level Agreements have become one of the most desirable objectives in cloud computing research and implementation. Since the fundamental feature of the cloud computing platform is hosting workloads with a variety of characteristics in a consolidated and on-demand manner, it is demanding to explore the inherent relationship between power usage and machine configurations. Subsequently, with an understanding of these inherent relationships, researchers are able to develop effective power management policies to optimize productivity by balancing power usage and system performance. In this dissertation, we develop an autonomic power-aware system management framework for large-scale computer systems. We propose a series of techniques including coarse-grain power profiling, VM power modelling, power-aware resource auto-configuration and full-system power usage simulator. These techniques help us to understand the characteristics of power consumption of various system components. Based on these techniques, we are able to test various job scheduling strategies and develop resource management approaches to enhance the systems' power efficiency.
Modern wireless communication systems use spectrally efficient modulation schemes to reach high data rate transmission. These schemes are generally involved with signals with high peak-to-average power ratio (PAPR). Moreover, the development of next generation wireless communication systems requires the power amplifiers to operate over a wide frequency band or multiple frequency bands to support different applications. These wide-band and multi-band solutions will lead to reductions in both the size and cost of the whole system. This dissertation presents several advanced power amplifier solutions to provide wide-band and multi-band operations with efficiency improvement at power back-offs.
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.
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.
Publicly available datasets in health science are often large and observational, in contrast to experimental datasets where a small number of data are collected in controlled experiments. Variables' causal relationships in the observational dataset are yet to be determined. However, there is a significant interest in health science to discover and analyze causal relationships from health data since identified causal relationships will greatly facilitate medical professionals to prevent diseases or to mitigate the negative effects of the disease. Recent advances in Computer Science, particularly in Bayesian networks, has initiated a renewed interest for causality research. Causal relationships can be possibly discovered through learning the network structures from data. However, the number of candidate graphs grows in a more than exponential rate with the increase of variables. Exact learning for obtaining the optimal structure is thus computationally infeasible in practice. As a result, heuristic approaches are imperative to alleviate the difficulty of computations. This research provides effective and efficient learning tools for local causal discoveries and novel methods of learning causal structures with a combination of background knowledge. Specifically in the direction of constraint based structural learning, polynomial-time algorithms for constructing causal structures are designed with first-order conditional independence. Algorithms of efficiently discovering non-causal factors are developed and proved. In addition, when the background knowledge is partially known, methods of graph decomposition are provided so as to reduce the number of conditioned variables. Experiments on both synthetic data and real epidemiological data indicate the provided methods are applicable to large-scale datasets and scalable for causal analysis in health data. Followed by the research methods and experiments, this dissertation gives thoughtful discussions on the reliability of causal discoveries computational health science research, complexity, and implications in health science research.
POD (Point of Dispensing)-based emergency response plans involving mass prophylaxis may seem feasible when considering the choice of dispensing points within a region, overall population density, and estimated traffic demands. However, the plan may fail to serve particular vulnerable sub-populations, resulting in access disparities during emergency response. Federal authorities emphasize on the need to identify sub-populations that cannot avail regular services during an emergency due to their special needs to ensure effective response. Vulnerable individuals require the targeted allocation of appropriate resources to serve their special needs. Devising schemes to address the needs of vulnerable sub-populations is essential for the effectiveness of response plans. This research focuses on data-driven computational methods to quantify and address vulnerabilities in response plans that require the allocation of targeted resources. Data-driven methods to identify and quantify vulnerabilities in response plans are developed as part of this research. Addressing vulnerabilities requires the targeted allocation of appropriate resources to PODs. The problem of resource allocation to PODs during public health emergencies is introduced and the variants of the resource allocation problem such as the spatial allocation, spatio-temporal allocation and optimal resource subset variants are formulated. Generating optimal resource allocation and scheduling solutions can be computationally hard problems. The application of metaheuristic techniques to find near-optimal solutions to the resource allocation problem in response plans is investigated. A vulnerability analysis and resource allocation framework that facilitates the demographic analysis of population data in the context of response plans, and the optimal allocation of resources with respect to the analysis are described.
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.
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.
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.
Computer scientists are increasingly aware of the power of ubiquitous computing systems that can display information in and about the user's environment. One sub category of ubiquitous computing is persuasive ambient information systems that involve an informative display transitioning between the periphery and center of attention. The goal of this ambient technology is to produce a behavior change, implying that a display must be informative, unobtrusive, and persuasive. While a significant body of research exists on ambient technology, previous research has not fully explored the different measures to identify behavior change, evaluation techniques for linking design characteristics to visual effectiveness, nor the use of short-term goals to affect long-term behavior change. This study uses the unique context of noise-induced hearing loss (NIHL) among collegiate musicians to explore these issues through developing the MIHL Reduction Feedback System that collects real-time data, translates it into visuals for music classrooms, provides predictive outcomes for goalsetting persuasion, and provides statistical measures of behavior change.
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.
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.
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.
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.
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 ...
Segmentation, recognition and 3D reconstruction of objects have been cutting-edge research topics, which have many applications ranging from environmental and medical to geographical applications as well as intelligent transportation. In this dissertation, I focus on the study of segmentation, recognition and 3D reconstruction of objects using LiDAR data/MRI. Three main works are that (I). Feature extraction algorithm based on sparse LiDAR data. A novel method has been proposed for feature extraction from sparse LiDAR data. The algorithm and the related principles have been described. Also, I have tested and discussed the choices and roles of parameters. By using correlation of neighboring points directly, statistic distribution of normal vectors at each point has been effectively used to determine the category of the selected point. (II). Segmentation and 3D reconstruction of objects based on LiDAR/MRI. The proposed method includes that the 3D LiDAR data are layered, that different categories are segmented, and that 3D canopy surfaces of individual tree crowns and clusters of trees are reconstructed from LiDAR point data based on a region active contour model. The proposed method allows for delineations of 3D forest canopy naturally from the contours of raw LiDAR point clouds. The proposed model is suitable not only for a series of ideal cone shapes, but also for other kinds of 3D shapes as well as other kinds dataset such as MRI. (III). Novel algorithms for recognition of objects based on LiDAR/MRI. Aimed to the sparse LiDAR data, the feature extraction algorithm has been proposed and applied to classify the building and trees. More importantly, the novel algorithms based on level set methods have been provided and employed to recognize not only the buildings and trees, the different trees (e.g. Oak trees and Douglas firs), but also the subthalamus nuclei (STNs). By using the novel algorithms based ...
As video games continue to become larger, more complex, and more costly to produce, research into methods to make game creation easier and faster becomes more valuable. One such research topic is procedural generation, which allows the computer to assist in the creation of content. This dissertation presents a new algorithm for the generation of Sokoban levels. Sokoban is a grid-based transport puzzle which is computational interesting due to being PSPACE-complete. Beyond just generating levels, the question of whether or not the levels created by this algorithm are interesting to human players is explored. A study was carried out comparing player attention while playing hand made levels versus their attention during procedurally generated levels. An auditory Stroop test was used to measure attention without disrupting play.
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 working at the optical frequency range. Second, inverse QCM method is proposed to implement flattened Luneburg lens antennas and parabolic antennas in the microwave range. Third, a dual-band compact directional coupler is realized by applying artificial transmission lines. In addition, a fully symmetrical coupler with artificial lumped element structure is also implemented. Finally, a tunable on-chip inductor, compact CMOS transmission lines, and metamaterial-based interconnects are proposed using artificial metal structures. All the proposed designs are simulated in full-wave 3D electromagnetic solvers, and the measurement results agree well with the simulation results. These artificial material-based novel design methodologies pave the way ...
The numerous surveillance videos recorded by a single stationary wide-angle-view camera persuade the use of a moving point as the representation of each small-size object in wide video scene. The sequence of the positions of each moving point can be used to generate a trajectory containing both spatial and temporal information of object's movement. In this study, we investigate how the relationship between two trajectories can be used to recognize multi-agent interactions. For this purpose, we present a simple set of qualitative atomic disjoint trajectory-segment relations which can be utilized to represent the relationships between two trajectories. Given a pair of adjacent concurrent trajectories, we segment the trajectory pair to get the ordered sequence of related trajectory-segments. Each pair of corresponding trajectory-segments then is assigned a token associated with the trajectory-segment relation, which leads to the generation of a string called a pairwise trajectory-segment relationship sequence. From a group of pairwise trajectory-segment relationship sequences, we utilize an unsupervised learning algorithm, particularly the k-medians clustering, to detect interesting patterns that can be used to classify lower-level multi-agent activities. We evaluate the effectiveness of the proposed approach by comparing the activity classes predicted by our method to the actual classes from the ground-truth set obtained using the crowdsourcing technique. The results show that the relationships between a pair of trajectories can signify the low-level multi-agent activities.
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 trajectories are clustered to construct mid-level actionlets, and the temporal relationships between actionlets are modeled as Action Graphs based on Allen interval predicates. The second is an effort for a novel and efficient representation of action graphs based on a sparse coding framework. Action graphs are first represented using Laplacian matrices and then decomposed as a linear combination of primitive dictionary items following sparse coding scheme. The optimization is eventually formulated and solved as a determinant maximization problem, and 1-nearest neighbor is used for action classification. The experiments have shown better results than existing approaches for regions-of-interest detection and action ...
This research is concerned with the identification of sentiment in multimodal content. This is of particular interest given the increasing presence of subjective multimodal content on the web and other sources, which contains a rich and vast source of people's opinions, feelings, and experiences. Despite the need for tools that can identify opinions in the presence of diverse modalities, most of current methods for sentiment analysis are designed for textual data only, and few attempts have been made to address this problem. The dissertation investigates techniques for augmenting linguistic representations with acoustic, visual, and physiological features. The potential benefits of using these modalities include linguistic disambiguation, visual grounding, and the integration of information about people's internal states. The main goal of this work is to build computational resources and tools that allow sentiment analysis to be applied to multimodal data. This thesis makes three important contributions. First, it shows that modalities such as audio, video, and physiological data can be successfully used to improve existing linguistic representations for sentiment analysis. We present a method that integrates linguistic features with features extracted from these modalities. Features are derived from verbal statements, audiovisual recordings, thermal recordings, and physiological sensors signals. The resulting multimodal sentiment analysis system is shown to significantly outperform the use of language alone. Using this system, we were able to predict the sentiment expressed in video reviews and also the sentiment experienced by viewers while exposed to emotionally loaded content. Second, the thesis provides evidence of the portability of the developed strategies to other affect recognition problems. We provided support for this by studying the deception detection problem. Third, this thesis contributes several multimodal datasets that will enable further research in sentiment and deception detection.
Mobile phones are one of the essential parts of modern life. Making a phone call is not the main purpose of a smart phone anymore, but merely one of many other features. Online social networking, chatting, short messaging, web browsing, navigating, and photography are some of the other features users enjoy in modern smartphones, most of which are provided by mobile apps. However, with this advancement, many security vulnerabilities have opened up in these devices. Malicious apps are a major threat for modern smartphones. According to Symantec Corp., by the middle of 2013, about 273,000 Android malware apps were identified. It is a complex issue to protect everyday users of mobile devices from the attacks of technologically competent hackers, illegitimate users, trolls, and eavesdroppers. This dissertation emphasizes the concept of intention identification. Then it looks into ways to utilize this intention identification concept to enforce security in a mobile phone platform. For instance, a battery monitoring app requiring SMS permissions indicates suspicious intention as battery monitoring usually does not need SMS permissions. Intention could be either the user's intention or the intention of an app. These intentions can be identified using their behavior or by using their source code. Regardless of the intention type, identifying it, evaluating it, and taking actions by using it to prevent any malicious intentions are the main goals of this research. The following four different security vulnerabilities are identified in this research: Malicious apps, spammers and lurkers in social networks, eavesdroppers in phone conversations, and compromised authentication. These four vulnerabilities are solved by detecting malware applications, identifying malicious users in a social network, enhancing the encryption system of a phone communication, and identifying user activities using electroencephalogram (EEG) for authentication. Each of these solutions are constructed using the idea of intention identification. Furthermore, many of ...
This dialog allows you to filter your current search.
Each of the Years listed note their name and the number of records that will be limited down to if you choose that option.
The list can be sorted by name or the count.
Having trouble finding an option within the list of Years? Start typing and we'll update the list to show only those items that match your needs.