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Ye, EnTeamWATCH: Visualizing Development Activities Using a 3-D City Metaphor to Improve Conflict Detection and Team Awareness
Doctor of Philosophy (PhD), Ohio University, 2017, Electrical Engineering & Computer Science (Engineering and Technology)
Awareness of others’ activities has been widely recognized as essential in facilitating coordination in a team among Computer-Supported Cooperative Work communities. Several field studies on software developers in large software companies such as Microsoft showed that coworker and artifact awareness are the most common information needs for software developers; however, they are also the most frequently unsatisfied information needs. As a result, they may duplicate work, or create conflicts without knowing the status of others and the whole project. To address this problem, we propose a new approach to visualize the developer’s activities using a 3-D city metaphor and implement it in a workspace awareness tool named TeamWATCH (Team based Workspace Awareness Toolkit and Collaboration Hub). TeamWATCH extracts awareness information of artifacts, revisions, and developers from their local workspaces, version control repository, and bug tracking system. It then visualizes both real time and history awareness information together in a 3-D common view shared by the whole team. It also highlights active artifacts that are being changed locally via eye-catching animations and provides the customized personal view for each developer. The main contributions of this dissertation are 1) a 3-D software visualization scheme that improves workspace awareness and enhances team collaboration; 2) the design and implementation of the workspace awareness tool TeamWATCH using this visualization scheme; and 3) evaluations of the effectiveness of such awareness tools using TeamWATCH as an example in maintaining project awareness and detecting and resolving conflicts via three controlled use experiments. The experiment results showed that the subjects using TeamWATCH performed significantly better in software revision history and project evolution comprehension, and early conflict detection and resolution.

Committee:

Chang Liu (Advisor)

Subjects:

Computer Science

Keywords:

Software collaboration; software visualization; workspace awareness

Alaql, Omar abdulrahmanGENERAL PURPOSE APPROACHES FOR NO-REFERENCE IMAGE QUALITY ASSESSMENT
PHD, Kent State University, 2017, College of Arts and Sciences / Department of Computer Science
The last decade has witnessed great advances in digital images. Massive numbers of digital images are being captured by mobile digital cameras due to the increasing popularity of mobile imaging devices. These images are subjected to many processing stages during storing, transmitting, or sharing over a network connection. Unfortunately, these processing stages could potentially add visual degradation to original image. These degradations reduce the perceived visual quality which leads to an unsatisfactory experience for human viewers. Therefore, Image Quality Assessment (IQA) has become a topic of high interest and intense research over the last decade. The aim of IQA is to automatically assess image quality in agreement with human judgments. This dissertation mainly focuses on the most challenging category of IQA - general- purpose No-Reference Image Quality Assessment (NR-IQA), where the goal is to assess the quality of images without information about the reference images and without prior knowledge about the types of distortions in the tested image. This dissertation contributes to the research of image quality assessment by proposing three novel approaches for NR- IQA and one model for image distortions classification. First, we propose improvements in image distortions classification by introducing a training model based on new features collection. Second, we propose a NR-IQA technique, which utilizes our improvement in the classification model, and based on a hypothesis that an effective combination of image features can be used to develop efficient NR-IQA approaches. Third, a NR-IQA technique is proposed based on Natural Scene Statistics (NSS) by finding the distance between the natural images and the distorted images in 3D dimensional space. Forth, a novel NR-IQA approach is presented, by utilizing multiple Deep Belief Networks (DBNs) with multiple regression models. We have evaluated the performance of the proposed and some existing models on a fair basis. The obtained results show that our models give better results and yield a significant improvement.

Committee:

Cheng-Chang Lu (Advisor); Austin Melton (Committee Member); Kambiz Ghazinour (Committee Member); Jun Li (Committee Member); Mohammed Khan (Committee Member)

Subjects:

Computer Science

Abounia Omran, BehzadApplication of Data Mining and Big Data Analytics in the Construction Industry
Doctor of Philosophy, The Ohio State University, 2016, Food, Agricultural and Biological Engineering
In recent years, the digital world has experienced an explosion in the magnitude of data being captured and recorded in various industry fields. Accordingly, big data management has emerged to analyze and extract value out of the collected data. The traditional construction industry is also experiencing an increase in data generation and storage. However, its potential and ability for adopting big data techniques have not been adequately studied. This research investigates the trends of utilizing big data techniques in the construction research community, which eventually will impact construction practice. For this purpose, the application of 26 popular big data analysis techniques in six different construction research areas (represented by 30 prestigious construction journals) was reviewed. Trends, applications, and their associations in each of the six research areas were analyzed. Then, a more in-depth analysis was performed for two of the research areas including construction project management and computation and analytics in construction to map the associations and trends between different construction research subjects and selected analytical techniques. In the next step, the results from trend and subject analysis were used to identify a promising technique, Artificial Neural Network (ANN), for studying two construction-related subjects, including prediction of concrete properties and prediction of soil erosion quantity in highway slopes. This research also compared the performance and applicability of ANN against eight predictive modeling techniques commonly used by other industries in predicting the compressive strength of environmentally friendly concrete. The results of this research provide a comprehensive analysis of the current status of applying big data analytics techniques in construction research, including trends, frequencies, and usage distribution in six different construction-related research areas, and demonstrate the applicability and performance level of selected data analytics techniques with an emphasis on ANN in construction-related studies. The main purpose of this dissertation was to help practitioners and researchers identify a suitable and applicable data analytics technique for their specific construction/research issue(s) or to provide insights into potential research directions.

Committee:

Qian Chen, Dr. (Advisor)

Subjects:

Civil Engineering; Comparative Literature; Computer Science

Keywords:

Construction Industry; Big Data; Data Analytics; Data mining; Artificial Neural Network; ANN; Compressive Strength; Environmentally Friendly Concrete; Soil Erosion; Highway Slope; Predictive Modeling; Comparative Analysis

Joshi, Amit KrishnaExploiting Alignments in Linked Data for Compression and Query Answering
Doctor of Philosophy (PhD), Wright State University, 2017, Computer Science and Engineering PhD
Linked data has experienced accelerated growth in recent years due to its interlinking ability across disparate sources, made possible via machine-processable RDF data. Today, a large number of organizations, including governments and news providers, publish data in RDF format, inviting developers to build useful applications through reuse and integration of structured data. This has led to tremendous increase in the amount of RDF data on the web. Although the growth of RDF data can be viewed as a positive sign for semantic web initiatives, it causes performance bottlenecks for RDF data management systems that store and provide access to data. In addition, a growing number of ontologies and vocabularies make retrieving data a challenging task. The aim of this research is to show how alignments in the Linked Data can be exploited to compress and query the linked datasets. First, we introduce two compression techniques that compress RDF datasets through identification and removal of semantic and contextual redundancies in linked data. Logical Linked Data Compression is a lossless compression technique which compresses a dataset by generating a set of new logical rules from the dataset and removing triples that can be inferred from these rules. Contextual Linked Data Compression is a lossy compression technique which compresses datasets by performing schema alignment and instance matching followed by pruning of alignments based on confidence value and subsequent grouping of equivalent terms. Depending on the structure of the dataset, the first technique was able to prune more than 50% of the triples. Second, we propose an Alignment based Linked Open Data Querying System (ALOQUS) that allows users to write query statements using concepts and properties not present in linked datasets and show that querying does not require a thorough understanding of the individual datasets and interconnecting relationships. Finally, we present LinkGen, a multipurpose synthetic Linked Data generator that generates a large amount of repeatable and reproducible RDF data using statistical distribution, and interlinks with real world entities using alignments.

Committee:

Pascal Hitzler , Ph.D. (Advisor); Guozhu Dong, Ph.D. (Committee Member); Krishnaprasad Thirunaraya, Ph.D. (Committee Member); Michelle Cheatham, Ph.D. (Committee Member); Subhashini Ganapathy, Ph.D. (Committee Member)

Subjects:

Computer Science

Keywords:

Linked Data; RDF Compression; Ontology Alignment; Linked Data Querying; Synthetic RDF Generator; SPARQL

Howard, Shaun MichaelDeep Learning for Sensor Fusion
Master of Sciences (Engineering), Case Western Reserve University, 2017, EECS - Computer and Information Sciences
The use of multiple sensors in modern day vehicular applications is necessary to provide a complete outlook of surroundings for advanced driver assistance systems (ADAS) and automated driving. The fusion of these sensors provides increased certainty in the recognition, localization and prediction of surroundings. A deep learning-based sensor fusion system is proposed to fuse two independent, multi-modal sensor sources. This system is shown to successfully learn the complex capabilities of an existing state-of-the-art sensor fusion system and generalize well to new sensor fusion datasets. It has high precision and recall with minimal confusion after training on several million examples of labeled multi-modal sensor data. It is robust, has a sustainable training time, and has real-time response capabilities on a deep learning PC with a single NVIDIA GeForce GTX 980Ti graphical processing unit (GPU).

Committee:

Wyatt Newman, Dr (Committee Chair); M. Cenk Cavusoglu, Dr (Committee Member); Michael Lewicki, Dr (Committee Member)

Subjects:

Artificial Intelligence; Computer Science

Keywords:

deep learning; sensor fusion; deep neural networks; advanced driver assistance systems; automated driving; multi-stream neural networks; feedforward; multilayer perceptron; recurrent; gated recurrent unit; long-short term memory; camera; radar;

Koya, Bharath KumarSched-ITS: An Interactive Tutoring System to Teach CPU Scheduling Concepts in an Operating Systems Course
Master of Science (MS), Wright State University, 2017, Computer Science
Operating systems is an essential course in computer science curriculum, which helps students to develop a mental model of how computer operating systems work. The internal mechanisms and processes of an operating system (OS) are often complex, non-deterministic and intangible which makes them difficult for students to understand. One such concept is central processing unit (CPU) scheduling. CPU scheduling forms the basis of the multiprogramming in an OS. In practice, OS courses involve classroom lectures describing high-level abstractions of the concepts, and students complete programming assignments to apply the material in a more concrete way. Depending on the programming assignments, this approach may leave students with only a theoretical understanding of OS ideas, which may be different from the actual way these concepts are implemented in an OS. What many students require is a practical knowledge of OS implementation to supplement the high-level presentations of concepts taught in class or presented in a textbook. To bridge the gap between the operating system theory and practical implementation, this research describes the development of an interactive simulation to present the theories involved in CPU scheduling in visualizations and simulations. This thesis discusses a prototype interactive tutoring system (ITS) named as Sched-ITS. The tool covers all the important algorithms of CPU scheduling such as first-come, first-serve (FCFS), round robin (RR), shortest job first (SJF), shortest remaining time first (SRTF), priority with pre-emption, and priority without pre-emption. Sched-ITS also provides graphical visualization of how context switches occur during CPU scheduling in a real operating system. Sched-ITS makes use of the JavaFX framework for visualization and Perf-tool for tracing an OS’s scheduling activities. It presents scheduling activities of background processes as well as pre-defined or user-defined processes. Sched-ITS can display scheduling order changes for different algorithms for the same set of processes in a Linux operating system.

Committee:

Adam R. Bryant, Ph.D. (Committee Chair); Mateen M. Rizki, Ph.D. (Committee Member); Yong Pei, Ph.D. (Committee Member)

Subjects:

Computer Science

Keywords:

CPU Scheduling Visualization; Linux Scheduler Visualization; Perf tool; Scheduler Trace Points; JavaFx

Chen, JitongOn Generalization of Supervised Speech Separation
Doctor of Philosophy, The Ohio State University, 2017, Computer Science and Engineering
Speech is essential for human communication as it not only delivers messages but also expresses emotions. In reality, speech is often corrupted by background noise and room reverberation. Perceiving speech in low signal-to-noise ratio (SNR) conditions is challenging, especially for hearing-impaired listeners. Therefore, we are motivated to develop speech separation algorithms to improve intelligibility of noisy speech. Given its many applications, such as hearing aids and robust automatic speech recognition (ASR), speech separation has been an important problem in speech processing for decades. Speech separation can be achieved by estimating the ideal binary mask (IBM) or ideal ratio mask (IRM). In a time-frequency (T-F) representation of noisy speech, the IBM preserves speech-dominant T-F units and discards noise-dominant ones. Similarly, the IRM adjusts the gain of each T-F unit to suppress noise. As such, speech separation can be treated as a supervised learning problem where one estimates the ideal mask from noisy speech. Three key components of supervised speech separation are learning machines, acoustic features and training targets. This supervised framework has enabled the treatment of speech separation with powerful learning machines such as deep neural networks (DNNs). For any supervised learning problem, generalization to unseen conditions is critical. This dissertation addresses generalization of supervised speech separation. We first explore acoustic features for supervised speech separation in low SNR conditions. An extensive list of acoustic features is evaluated for IBM estimation. The list includes ASR features, speaker recognition features and speech separation features. In addition, we propose the Multi-Resolution Cochleagram (MRCG) feature to incorporate both local information and broader spectrotemporal contexts. We find that gammatone-domain features, especially the proposed MRCG features, perform well for supervised speech separation at low SNRs. Noise segment generalization is desired for noise-dependent speech separation. When tested on the same noise type, a learning machine needs to generalize to unseen noise segments. For nonstationary noises, there exists a considerable mismatch between training and testing segments, which leads to poor performance during testing. We explore noise perturbation techniques to expand training noise for better generalization. Experiments show that frequency perturbation effectively reduces false-alarm errors in mask estimation and leads to improved objective metrics of speech intelligibility. Speech separation in unseen environments requires generalization to unseen noise types, not just noise segments. By exploring large-scale training, we find that a DNN based IRM estimator trained on a large variety of noises generalizes well to unseen noises. Even for highly nonstationary noises, the noise-independent model achieves similar performance as noise-dependent models in terms of objective speech intelligibility measures. Further experiments with human subjects lead to the first demonstration that supervised speech separation improves speech intelligibility for hearing-impaired listeners in novel noises. Besides noise generalization, speaker generalization is critical for many applications where target speech may be produced by an unseen speaker. We observe that training a DNN with many speakers leads to poor speaker generalization. The performance on seen speakers degrades as additional speakers are added for training. Such a DNN suffers from the confusion of target speech and interfering speech fragments embedded in noise. We propose a model based on recurrent neural network (RNN) with long short-term memory (LSTM) to incorporate the temporal dynamics of speech. We find that the trained LSTM keeps track of a target speaker and substantially improves speaker generalization over DNN. Experiments show that the proposed model generalizes to unseen noises, unseen SNRs and unseen speakers.

Committee:

DeLiang Wang (Advisor); Eric Fosler-Lussier (Committee Member); Eric Healy (Committee Member)

Subjects:

Computer Science; Engineering

Keywords:

Speech separation; speech intelligibility; computational auditory scene analysis; mask estimation; supervised learning; deep neural networks; acoustic features; noise generalization; SNR generalization; speaker generalization;

Liu, YatingMotif Selection via a Tabu Search Solution to the Set Cover Problem
Master of Science (MS), Ohio University, 2017, Computer Science (Engineering and Technology)
Transcription factors (TFs) regulate gene expression through interaction with specific DNA regions, called transcription factor binding sites (TFBSs). Identifying TFBSs can help in understanding the mechanisms of gene regulation and the biology of human diseases. Motif discovery is the traditional method for discovering TFBSs. However, current motif discovery tools tend to generate a number of motifs that is too large to permit a biological validation. To address this problem, the motif selection problem is introduced. The aim of the motif selection problem is to select a small set of motifs from the discovered motifs, which cover a high percentage of genomic input sequences. Tabu search, a metaheuristic search method based on local search, is introduced to solve the motif selection problem. The performance of the proposed three motif selection methods, tabu-SCP, tabu-PSC and tabu-PNPSC, were evaluated by applying them to ChIP-seq data from the ENCyclopedia of DNA Elements (ENCODE) project. Motif selection was performed on 46 factor groups which include 158 human ChIP-seq data sets. The results of the three motif selection methods were compared with Greedy, enrichment method and relax integer liner programming (RILP). Tabu-PNPSC selected the smallest set of motifs with the highest overall accuracy. The average number of selected motifs was 1.37 and the average accuracy was 72.47%. Tabu-PNPSC was used to identify putative regulatory element binding sites that are in response to the overproduction of small RNAs RyfA1 in the bacteria Shigella dysenteriae. Six motifs were selected by tabu-PNPSC and the overall accuracy was 75.5%.

Committee:

Lonnie Welch (Advisor)

Subjects:

Bioinformatics; Computer Science

Keywords:

motif selection; tabu search; set cover problem

Jenson, SageDigital Morphologies: Environmentally-Influenced Generative Forms
BA, Oberlin College, 2017, Computer Science
We present a generative method to grow triangular meshes with organically-shaped features. Through the application of simplified forces, millions of particles develop into complex 3D forms in silico. These forms interact with external environments in a variety of ways, allowing for the integration of the proposed technique with pre-existing 3D objects and scenes. Large simulation sizes were computationally achieved through the massively parallel capabilities of modern Graphics Processing Units (GPUs).

Committee:

Robert Bosch (Advisor); Tom Wexler (Advisor)

Subjects:

Computer Science

Keywords:

graphics, 3D, coral, morphology, GPGPU, GPU, parallel computing, CUDA, simulation, organic, growth, particles, collision detection

Billa, Anka BabuDevelopment of an Ultra-Portable Non-Contact Wound Measurement System
Master of Science (MS), Wright State University, 2017, Computer Science
Continuous monitoring of changes in wound size is key to correctly predict whether wounds will heal readily with conventional treatment or require more aggressive treatment strategies. Unfortunately, existing wound measurement solutions don’t meet the clinical demand due to their limitations in accuracy, operating complexity and time, acquisition and operation cost, or reproducibility, resulting in unnecessarily lengthy recovery or extra treatment procedures, incurring an excessively high financial cost, and in many cases extended usage of addictive painkillers. In this thesis, we proposed and developed a low cost, a portable non-contact solution that combines multi-spectral imaging and a portfolio of imaging processing technologies to enable automatic and instantaneous wound identification and measurements. It provides full measurements of a wound: surface area, perimeter, length, and width, without requiring the calibration process as other existing photogrammetry or laser solutions. We have developed a prototype system that illustrates our image and wound analysis capabilities using off-shelf sensor units for capturing images. Our system is capable of identifying emulated wounds in any part of human body surface automatically and highlights them on a customized GUI instantly. Image processing engine running in background analyze and computes wound dimensions with an accuracy of 95%. Our experiment results indicated that the system is reliable, consistent, accurate and reproducible. This research has recently been selected to the 2017 I-Corps@Ohio program, a statewide program to assist faculty and graduate students from Ohio universities and colleges in validating the market potential of their technologies and assisting with launching startup companies.

Committee:

Yong Pei, Ph.D. (Advisor); Mateen Rizki, Ph.D. (Committee Member); Krishnaprasad Thirunarayan, Ph.D. (Committee Member)

Subjects:

Computer Science

Keywords:

Wound Dimensions Measurement; Distance between camera and object; Image Processing; Stereo Image pair Disparity; Feature based Matching

Hall, Brenton TaylorUsing the Non-Uniform Dynamic Mode Decomposition to Reduce the Storage Required for PDE Simulations
Master of Mathematical Sciences, The Ohio State University, 2017, Mathematical Sciences
Partial Differential Equation simulations can produce large amounts of data that are very slow to transfer. There have been many model reduction techniques that have been proposed and utilized over the past three decades. Two popular techniques Proper Orthogonal Decomposition and Dynamic Mode Decomposition have some hindrances. Non-Uniform Dynamic Mode Decomposition (NU-DMD), which was introduced in 2015 by Gueniat et al., that overcomes some of these hindrances. In this thesis, the NU-DMD's mathematics are explained in detail, and three versions of the NU-DMD's algorithm are outlined. Furthermore, different numerical experiments were performed on the NU-DMD to ascertain its behavior with repect to errors, memory usage, and computational efficiency. It was shown that the NU-DMD could reduce an advection-diffusion simulation to 6.0075% of its original memory storage size. The NU-DMD was also applied to a computational fluid dynamics simulation of a NASA single-stage compressor rotor, which resulted in a reduced model of the simulation (using only three of the five simulation variables) that used only about 4.67% of the full simulation's storage with an overall average percent error of 8.90%. It was concluded that the NU-DMD, if used appropriately, could be used to possibly reduce a model that uses 400GB of memory to a model that uses as little as 18.67GB with less than 9% error. Further conclusions were made about how to best implement the NU-DMD.

Committee:

Ching-Shan Chou (Advisor); Jen-Ping Chen (Committee Member)

Subjects:

Aerospace Engineering; Applied Mathematics; Computer Science; Mathematics; Mechanical Engineering

Keywords:

Fluid Dynamics; Fluid Flow; Model Reduction; Partial Differential Equations; reducing memory; Dynamic Mode Decomposition; Decomposition; memory; Non-Uniform Dynamic Mode Decomposition

Kim, Dae WookData-Driven Network-Centric Threat Assessment
Doctor of Philosophy (PhD), Wright State University, 2017, Computer Science and Engineering PhD
As the Internet has grown increasingly popular as a communication and information sharing platform, it has given rise to two major types of Internet security threats related to two primary entities: end-users and network services. First, information leakages from networks can reveal sensitive information about end-users. Second, end-users systems can be compromised through attacks on network services, such as scanning-and-exploit attacks, spamming, drive-by downloads, and fake anti-virus software. Designing threat assessments to detect these threats is, therefore, of great importance, and a number of the detection systems have been proposed. However, these existing threat assessment systems face significant challenges in terms of i) behavioral diversity, ii) data heterogeneity, and iii) large data volume. To address the challenges of the two major threat types, this dissertation offers three unique contributions. First, we built a new system to identify network users via Domain Name System (DNS) traffic, which is one of the most important behavior-based tracking methods for addressing privacy threats. The goal of our system is to boost the effectiveness of existing user identification systems by designing effective fingerprint patterns based on semantically limited DNS queries that are missed by existing tracking efforts. Second, we built a novel system to detect fake anti-virus (AV) attacks, which represent an active trend in the distribution of Internet-based malware. Our system aims to boost the effectiveness of existing fake AV attack detection by detecting fake AV attacks in three challenging scenarios: i) fake AV webpages that require user interaction to install malware, instead of using malicious content to run automatic exploitation without users consent (e.g., shellcode); ii) fake AV webpages designed to impersonate real webpages using a few representative elements, such as the names and icons of anti-virus products from authentic anti-virus webpages; and iii) fake AV webpages that offer up-to-date solutions (e.g.,product versions and threat names) to emerging threats. Finally, we built a novel system to detect malicious online social network (OSN) accounts that participate in online promotion events. The goal of our work is to boost the effectiveness of existing detection methods, such as spammer detection and fraud detection. To achieve our goal, our framework that systematically integrates features that characterize malicious OSN accounts based on three of their characteristics: their general behaviors, their recharging patterns, and their currency usage, and then leverages statistical classifier for detection.

Committee:

Junjie Zhang, Ph.D. (Advisor); Adam Robert Bryant, Ph.D. (Committee Member); Bin Wang, Ph.D. (Committee Member); Xuetao Wei, Ph.D. (Committee Member)

Subjects:

Computer Science

Keywords:

network security; fake anti-virus software; intrusion detection; web document analysis; statistical classification; Domain Name System; behavioral fingerprints; privacy; online social networks; virtual currency; malicious accounts

Chen, ZhiangDeep-learning Approaches to Object Recognition from 3D Data
Master of Sciences, Case Western Reserve University, 2017, EMC - Mechanical Engineering
This thesis focuses on deep-learning approaches to recognition and pose estimation of graspable objects using depth information. Recognition and orientation detection from depth-only data is encoded by a carefully designed 2D descriptor from 3D point clouds. Deep-learning approaches are explored from two main directions: supervised learning and semi-supervised learning. The disadvantages of supervised learning approaches drive the exploration of unsupervised pretraining. By learning good representations embedded in early layers, subsequent layers can be trained faster and with better performance. An understanding of learning processes from a probabilistic perspective is concluded, and it paves the way for developing networks based on Bayesian models, including Variational Auto-Encoders. Exploitation of knowledge transfer--re-using parameters learned from alternative training data--is shown to be effective in the present application.

Committee:

Wyatt Newman, PhD (Advisor); M. Cenk Çavusoglu, PhD (Committee Member); Roger Quinn, PhD (Committee Member)

Subjects:

Computer Science; Medical Imaging; Nanoscience; Robotics

Keywords:

deep learning; 3D object recognition; semi-supervised learning; knowledge transfer

Bettaieb, Luc AlexandreA Deep Learning Approach To Coarse Robot Localization
Master of Sciences (Engineering), Case Western Reserve University, 2017, EECS - Electrical Engineering
This thesis explores the use of deep learning for robot localization with applications in re-localizing a mislocalized robot. Seed values for a localization algorithm are assigned based on the interpretation of images. A deep neural network was trained on images acquired in and associated with named regions. In application, the neural net was used to recognize a region based on camera input. By recognizing regions from the camera, the robot can be localized grossly, and subsequently refined with existing techniques. Explorations into different deep neural network topologies and solver types are discussed. A process for gathering training data, training the classifier, and deployment through a robot operating system (ROS) package is provided.

Committee:

Wyatt Newman (Advisor); Murat Cavusoglu (Committee Member); Gregory Lee (Committee Member)

Subjects:

Computer Science; Electrical Engineering; Robotics

Keywords:

robotics; localization; deep learning; neural networks; machine learning; state estimation; robots; robot; robot operating system; ROS; AMCL; monte carlo localization; particle filter; ConvNets; convolutional neural networks

Subramanian, RamachandranPerformance analysis of cache coherence protocols in shared-memory multiprocessor systems under generalized access environments /
Doctor of Philosophy, The Ohio State University, 1996, Graduate School

Committee:

Not Provided (Other)

Subjects:

Computer Science

Bylander, Thomas C.Consolidation : a method for reasoning about the behavior of devices /
Doctor of Philosophy, The Ohio State University, 1986, Graduate School

Committee:

Not Provided (Other)

Subjects:

Computer Science

Keywords:

Artificial intelligence;Physics;Reasoning;Conduct of life

Ayen, William EugenePerformance measurement in a distributed processing environment /
Doctor of Philosophy, The Ohio State University, 1984, Graduate School

Committee:

Not Provided (Other)

Subjects:

Computer Science

Keywords:

Electronic data processing--Distributed processing;Performance standards

Manivannan, D.Quasi-synchronous checkpointing and failure recovery in distributed systems /
Doctor of Philosophy, The Ohio State University, 1997, Graduate School

Committee:

Not Provided (Other)

Subjects:

Computer Science

Lee, Wha-JoonA computer simulation study of omnidirectional supervisory control for rough-terrain locomotion by a multilegged robot vehicle/
Doctor of Philosophy, The Ohio State University, 1984, Graduate School

Committee:

Not Provided (Other)

Subjects:

Computer Science

Keywords:

Robots;Locomotion;Computer simulation

Subramanian, NanditaAnalysis of Rank Distance for Malware Classification
MS, University of Cincinnati, 2016, Engineering and Applied Science: Computer Science
Malicious Cyber Adversaries may compromise the security of a system by denying access to legitimate users. This is often coupled with immeasurable loss of confidential data, which leads to hefty losses in both financial and trustworthiness aspects of a corporation. Malware exploits key vulnerabilities in applications presenting problems such as identity theft, unapproved software installations, etc. Abundance in malware detection and removal techniques in the ever evolving field of computers, presently exhibit a lower level of efficiency in detecting malicious softwares. Techniques available currently enable detection of softwares that are embedded with known signatures. No doubt these methods are efficient. However, most malware writers, aware of signature-based detection methods are working towards bypassing them. Machine learning based systems for malware classification and detection have been tested and proved to be more efficient than standard signature-based systems. A vital reason and justification providing a strong foothold for using machine learning techniques is that even unseen malware can be detected, thus eliminating malware detection failures and providing very high success rates. Our method uses efficient machine learning techniques for classification and detection of portable executable (PE) files of various malware classes commonly found in computers running Windows operating systems. For malicious files, computation of the distance between two files should yield an indication of their similarity. Using this as a basis, this thesis analyses the different approaches which can be employed for classifying malicious files using a method known as rank distance. This distance measure has been combined with a feature extraction method known as mutual information which analyses the opcodes n-gram sequences extracted from the PE files and segregates the most relevant opcodes from these. The most relevant opcodes, thus obtained, are used as features to identify which class a given file belongs to. An opcode relevance profile generated based on mutual information and the unclassified file are compared and assigned the respective rank distances for every class. Using these ranks, a distance between the two files is obtained. The class which has the least distance to the file is concluded to be the class of the file under scrutiny.

Committee:

Anca Ralescu, Ph.D. (Committee Chair); Chia Han, Ph.D. (Committee Member); Dan Ralescu, Ph.D. (Committee Member)

Subjects:

Computer Science

Keywords:

Rank Distance;Malware Classification;Mutual Information;Text Mining;Similarity Measures;Windows Malware

Ordonez, IvánSTA : Spatio-Temporal Aggregation of physical fields with applications to analysis of diffusion-reaction phenomena /
Doctor of Philosophy, The Ohio State University, 1999, Graduate School

Committee:

Not Provided (Other)

Subjects:

Computer Science

Hochstettler, William HenryA model for supporting multiple software engineering methods in a software environment /
Doctor of Philosophy, The Ohio State University, 1986, Graduate School

Committee:

Not Provided (Other)

Subjects:

Computer Science

Keywords:

Computer programming;Computer software

Mandal, ManasEfficient distributed shared memory using mapped segmentation and reusable single-assignment variables /
Doctor of Philosophy, The Ohio State University, 1995, Graduate School

Committee:

Not Provided (Other)

Subjects:

Computer Science

Shomper, Keith A.Visualizing program variable data for debugging /
Doctor of Philosophy, The Ohio State University, 1993, Graduate School

Committee:

Not Provided (Other)

Subjects:

Computer Science

Babbar, DavenderOn-line hard real-time scheduling of parallel tasks on partitionable multiprocessors /
Doctor of Philosophy, The Ohio State University, 1994, Graduate School

Committee:

Not Provided (Other)

Subjects:

Computer Science

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