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Adaptive Power Management for Autonomic Resource Configuration in Large-scale Computer Systems

Description: 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.
Date: August 2015
Creator: Zhang, Ziming

Advanced Power Amplifiers Design for Modern Wireless Communication

Description: 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.
Date: August 2015
Creator: Shao, Jin

Algorithm Optimizations in Genomic Analysis Using Entropic Dissection

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

Automated Classification of Emotions Using Song Lyrics

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

Automated Real-time Objects Detection in Colonoscopy Videos for Quality Measurements

Description: The effectiveness of colonoscopy depends on the quality of the inspection of the colon. There was no automated measurement method to evaluate the quality of the inspection. This thesis addresses this issue by investigating an automated post-procedure quality measurement technique and proposing a novel approach automatically deciding a percentage of stool areas in images of digitized colonoscopy video files. It involves the classification of image pixels based on their color features using a new method of planes on RGB (red, green and blue) color space. The limitation of post-procedure quality measurement is that quality measurements are available long after the procedure was done and the patient was released. A better approach is to inform any sub-optimal inspection immediately so that the endoscopist can improve the quality in real-time during the procedure. This thesis also proposes an extension to post-procedure method to detect stool, bite-block, and blood regions in real-time using color features in HSV color space. These three objects play a major role in quality measurements in colonoscopy. The proposed method partitions very large positive examples of each of these objects into a number of groups. These groups are formed by taking intersection of positive examples with a hyper plane. This hyper plane is named as 'positive plane'. 'Convex hulls' are used to model positive planes. Comparisons with traditional classifiers such as K-nearest neighbor (K-NN) and support vector machines (SVM) proves the soundness of the proposed method in terms of accuracy and speed that are critical in the targeted real-time quality measurement system.
Date: August 2013
Creator: Kumara, Muthukudage Jayantha

Automatic Removal of Complex Shadows From Indoor Videos

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

Automatic Tagging of Communication Data

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

Autonomic Failure Identification and Diagnosis for Building Dependable Cloud Computing Systems

Description: The increasingly popular cloud-computing paradigm provides on-demand access to computing and storage with the appearance of unlimited resources. Users are given access to a variety of data and software utilities to manage their work. Users rent virtual resources and pay for only what they use. In spite of the many benefits that cloud computing promises, the lack of dependability in shared virtualized infrastructures is a major obstacle for its wider adoption, especially for mission-critical applications. Virtualization and multi-tenancy increase system complexity and dynamicity. They introduce new sources of failure degrading the dependability of cloud computing systems. To assure cloud dependability, in my dissertation research, I develop autonomic failure identification and diagnosis techniques that are crucial for understanding emergent, cloud-wide phenomena and self-managing resource burdens for cloud availability and productivity enhancement. We study the runtime cloud performance data collected from a cloud test-bed and by using traces from production cloud systems. We define cloud signatures including those metrics that are most relevant to failure instances. We exploit profiled cloud performance data in both time and frequency domain to identify anomalous cloud behaviors and leverage cloud metric subspace analysis to automate the diagnosis of observed failures. We implement a prototype of the anomaly identification system and conduct the experiments in an on-campus cloud computing test-bed and by using the Google datacenter traces. Our experimental results show that our proposed anomaly detection mechanism can achieve 93% detection sensitivity while keeping the false positive rate as low as 6.1% and outperform other tested anomaly detection schemes. In addition, the anomaly detector adapts itself by recursively learning from these newly verified detection results to refine future detection.
Date: May 2014
Creator: Guan, Qiang

Classifying Pairwise Object Interactions: A Trajectory Analytics Approach

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

A Computational Methodology for Addressing Differentiated Access of Vulnerable Populations During Biological Emergencies

Description: Mitigation response plans must be created to protect affected populations during biological emergencies resulting from the release of harmful biochemical substances. Medical countermeasures have been stockpiled by the federal government for such emergencies. However, it is the responsibility of local governments to maintain solid, functional plans to apply these countermeasures to the entire target population within short, mandated time frames. Further, vulnerabilities in the population may serve as barriers preventing certain individuals from participating in mitigation activities. Therefore, functional response plans must be capable of reaching vulnerable populations.Transportation vulnerability results from lack of access to transportation. Transportation vulnerable populations located too far from mitigation resources are at-risk of not being able to participate in mitigation activities. Quantification of these populations requires the development of computational methods to integrate spatial demographic data and transportation resource data from disparate sources into the context of planned mitigation efforts. Research described in this dissertation focuses on quantifying transportation vulnerable populations and maximizing participation in response efforts. Algorithms developed as part of this research are integrated into a computational framework to promote a transition from research and development to deployment and use by biological emergency planners.
Date: August 2014
Creator: O’Neill II, Martin Joseph

Computational Methods for Discovering and Analyzing Causal Relationships in Health Data

Description: 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.
Date: August 2015
Creator: Liang, Yiheng

Computational Methods for Vulnerability Analysis and Resource Allocation in Public Health Emergencies

Description: 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.
Date: August 2015
Creator: Indrakanti, Saratchandra

Ddos Defense Against Botnets in the Mobile Cloud

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

Design and Analysis of Novel Verifiable Voting Schemes

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

Design and Implementation of Large-Scale Wireless Sensor Networks for Environmental Monitoring Applications

Description: Environmental monitoring represents a major application domain for wireless sensor networks (WSN). However, despite significant advances in recent years, there are still many challenging issues to be addressed to exploit the full potential of the emerging WSN technology. In this dissertation, we introduce the design and implementation of low-power wireless sensor networks for long-term, autonomous, and near-real-time environmental monitoring applications. We have developed an out-of-box solution consisting of a suite of software, protocols and algorithms to provide reliable data collection with extremely low power consumption. Two wireless sensor networks based on the proposed solution have been deployed in remote field stations to monitor soil moisture along with other environmental parameters. As parts of the ever-growing environmental monitoring cyberinfrastructure, these networks have been integrated into the Texas Environmental Observatory system for long-term operation. Environmental measurement and network performance results are presented to demonstrate the capability, reliability and energy-efficiency of the network.
Date: May 2010
Creator: Yang, Jue

The Design Of A Benchmark For Geo-stream Management Systems

Description: The recent growth in sensor technology allows easier information gathering in real-time as sensors have grown smaller, more accurate, and less expensive. The resulting data is often in a geo-stream format continuously changing input with a spatial extent. Researchers developing geo-streaming management systems (GSMS) require a benchmark system for evaluation, which is currently lacking. This thesis presents GSMark, a benchmark for evaluating GSMSs. GSMark provides a data generator that creates a combination of synthetic and real geo-streaming data, a workload simulator to present the data to the GSMS as a data stream, and a set of benchmark queries that evaluate typical GSMS functionality and query performance. In particular, GSMark generates both moving points and evolving spatial regions, two fundamental data types for a broad range of geo-stream applications, and the geo-streaming queries on this data.
Date: December 2011
Creator: Shen, Chao

Detection of Temporal Events and Abnormal Images for Quality Analysis in Endoscopy Videos

Description: Recent reports suggest that measuring the objective quality is very essential towards the success of colonoscopy. Several quality indicators (i.e. metrics) proposed in recent studies are implemented in software systems that compute real-time quality scores for routine screening colonoscopy. Most quality metrics are derived based on various temporal events occurred during the colonoscopy procedure. The location of the phase boundary between the insertion and the withdrawal phases and the amount of circumferential inspection are two such important temporal events. These two temporal events can be determined by analyzing various camera motions of the colonoscope. This dissertation put forward a novel method to estimate X, Y and Z directional motions of the colonoscope using motion vector templates. Since abnormalities of a WCE or a colonoscopy video can be found in a small number of frames (around 5% out of total frames), it is very helpful if a computer system can decide whether a frame has any mucosal abnormalities. Also, the number of detected abnormal lesions during a procedure is used as a quality indicator. Majority of the existing abnormal detection methods focus on detecting only one type of abnormality or the overall accuracies are somewhat low if the method tries to detect multiple abnormalities. Most abnormalities in endoscopy images have unique textures which are clearly distinguishable from normal textures. In this dissertation a new method is proposed that achieves the objective of detecting multiple abnormalities with a higher accuracy using a multi-texture analysis technique. The multi-texture analysis method is designed by representing WCE and colonoscopy image textures as textons.
Date: August 2013
Creator: Nawarathna, Ruwan D.

Detection of Ulcerative Colitis Severity and Enhancement of Informative Frame Filtering Using Texture Analysis in Colonoscopy Videos

Description: 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.
Date: December 2015
Creator: Dahal, Ashok

Distributed Frameworks Towards Building an Open Data Architecture

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

Effective and Accelerated Informative Frame Filtering in Colonoscopy Videos Using Graphic Processing Units

Description: Colonoscopy is an endoscopic technique that allows a physician to inspect the mucosa of the human colon. Previous methods and software solutions to detect informative frames in a colonoscopy video (a process called informative frame filtering or IFF) have been hugely ineffective in (1) covering the proper definition of an informative frame in the broadest sense and (2) striking an optimal balance between accuracy and speed of classification in both real-time and non real-time medical procedures. In my thesis, I propose a more effective method and faster software solutions for IFF which is more effective due to the introduction of a heuristic algorithm (derived from experimental analysis of typical colon features) for classification. It contributed to a 5-10% boost in various performance metrics for IFF. The software modules are faster due to the incorporation of sophisticated parallel-processing oriented coding techniques on modern microprocessors. Two IFF modules were created, one for post-procedure and the other for real-time. Code optimizations through NVIDIA CUDA for GPU processing and/or CPU multi-threading concepts embedded in two significant microprocessor design philosophies (multi-core design and many-core design) resulted a 5-fold acceleration for the post-procedure module and a 40-fold acceleration for the real-time module. Some innovative software modules, which are still in testing phase, have been recently created to exploit the power of multiple GPUs together.
Date: August 2010
Creator: Karri, Venkata Praveen

Elicitation of Protein-Protein Interactions from Biomedical Literature Using Association Rule Discovery

Description: Extracting information from a stack of data is a tedious task and the scenario is no different in proteomics. Volumes of research papers are published about study of various proteins in several species, their interactions with other proteins and identification of protein(s) as possible biomarker in causing diseases. It is a challenging task for biologists to keep track of these developments manually by reading through the literatures. Several tools have been developed by computer linguists to assist identification, extraction and hypotheses generation of proteins and protein-protein interactions from biomedical publications and protein databases. However, they are confronted with the challenges of term variation, term ambiguity, access only to abstracts and inconsistencies in time-consuming manual curation of protein and protein-protein interaction repositories. This work attempts to attenuate the challenges by extracting protein-protein interactions in humans and elicit possible interactions using associative rule mining on full text, abstracts and captions from figures available from publicly available biomedical literature databases. Two such databases are used in our study: Directory of Open Access Journals (DOAJ) and PubMed Central (PMC). A corpus is built using articles based on search terms. A dataset of more than 38,000 protein-protein interactions from the Human Protein Reference Database (HPRD) is cross-referenced to validate discovered interactive pairs. A set of an optimal size of possible binary protein-protein interactions is generated to be made available for clinician or biological validation. A significant change in the number of new associations was found by altering the thresholds for support and confidence metrics. This study narrows down the limitations for biologists in keeping pace with discovery of protein-protein interactions via manually reading the literature and their needs to validate each and every possible interaction.
Date: August 2010
Creator: Samuel, Jarvie John

An Empirical Study of How Novice Programmers Use the Web

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

Evaluating Appropriateness of Emg and Flex Sensors for Classifying Hand Gestures

Description: 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 directions simultaneously. EMG sensors are placed on the upper and lower forearm and assist in the classification of the finger and wrist movements. Bend sensors are mounted on a glove that is worn on the hand. The sensors are located over the first knuckle of each figure and can determine if the finger is bent or not. An accelerometer is attached to the glove at the base of the wrist and determines the speed and direction of the arm movement. Classification algorithm SVM is used to classify the gestures.
Date: May 2013
Creator: Akumalla, Sarath Chandra

Evaluation Techniques and Graph-Based Algorithms for Automatic Summarization and Keyphrase Extraction

Description: 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 ...
Date: August 2016
Creator: Hamid, Fahmida